Prompt:
Problem:
I have a data frame like below
    A_Name  B_Detail  Value_B  Value_C   Value_D ......
0   AA      X1        1.2      0.5       -1.3    ......
1   BB      Y1        0.76     -0.7      0.8     ......
2   CC      Z1        0.7      -1.3      2.5     ......
3   DD      L1        0.9      -0.5      0.4     ......
4   EE      M1        1.3      1.8       -1.3    ......
5   FF      N1        0.7      -0.8      0.9     ......
6   GG      K1        -2.4     -1.9      2.1     ......

This is just a sample of data frame, I can have n number of columns like (Value_A, Value_B, Value_C, ........... Value_N)
Now i want to filter all rows where absolute value of any columns (Value_A, Value_B, Value_C, ....) is more than 1.
If you have limited number of columns, you can filter the data by simply putting 'or' condition on columns in dataframe, but I am not able to figure out what to do in this case.
I don't know what would be number of such columns, the only thing I know that such columns would be prefixed with 'Value'.
In above case output should be like
  A_Name B_Detail  Value_B  Value_C  Value_D
0     AA       X1      1.2      0.5     -1.3
2     CC       Z1      0.7     -1.3      2.5
4     EE       M1      1.3      1.8     -1.3
6     GG       K1     -2.4     -1.9      2.1

A:
<code>
import pandas as pd

df = pd.DataFrame({'A_Name': ['AA', 'BB', 'CC', 'DD', 'EE', 'FF', 'GG'],
                   'B_Detail': ['X1', 'Y1', 'Z1', 'L1', 'M1', 'N1', 'K1'],
                   'Value_B': [1.2, 0.76, 0.7, 0.9, 1.3, 0.7, -2.4],
                   'Value_C': [0.5, -0.7, -1.3, -0.5, 1.8, -0.8, -1.9],
                   'Value_D': [-1.3, 0.8, 2.5, 0.4, -1.3, 0.9, 2.1]})
</code>
df = ... # put solution in this variable
BEGIN SOLUTION
<code>

def g(df):
    mask = (df.filter(like='Value').abs() > 1).any(axis=1)
    return df[mask]

df = g(df.copy())

import pandas as pd
import numpy as np
import copy

def generate_test_case(test_case_id):
    def generate_ans(data):
        df = data
        mask = (df.filter(like="Value").abs() > 1).any(axis=1)
        return df[mask]

    def define_test_input(test_case_id):
        if test_case_id == 1:
            df = pd.DataFrame(
                {
                    "A_Name": ["AA", "BB", "CC", "DD", "EE", "FF", "GG"],
                    "B_Detail": ["X1", "Y1", "Z1", "L1", "M1", "N1", "K1"],
                    "Value_B": [1.2, 0.76, 0.7, 0.9, 1.3, 0.7, -2.4],
                    "Value_C": [0.5, -0.7, -1.3, -0.5, 1.8, -0.8, -1.9],
                    "Value_D": [-1.3, 0.8, 2.5, 0.4, -1.3, 0.9, 2.1],
                }
            )
        elif test_case_id == 2:
            df = pd.DataFrame(
                {
                    "A_Name": ["AA", "BB", "CC", "DD", "EE", "FF", "GG"],
                    "B_Detail": ["X1", "Y1", "Z1", "L1", "M1", "N1", "K1"],
                    "Value_B": [1.2, 0.76, 0.7, 0.9, 1.3, 0.7, -2.4],
                    "Value_C": [0.5, -0.7, -1.3, -0.5, 1.8, -0.8, -1.9],
                    "Value_D": [-1.3, 0.8, 2.5, 0.4, -1.3, 0.9, 2.1],
                    "Value_E": [1, 1, 4, -5, -1, -4, 2.1],
                    "Value_F": [-1.9, 2.6, 0.8, 1.7, -1.3, 0.9, 2.1],
                }
            )
        return df

    test_input = define_test_input(test_case_id)
    expected_result = generate_ans(copy.deepcopy(test_input))
    return test_input, expected_result

def exec_test(result, ans):
    try:
        pd.testing.assert_frame_equal(result, ans, check_dtype=False)
        return 1
    except:
        return 0

exec_context = r"""
import pandas as pd
import numpy as np
df = test_input
[insert]
result = df
"""

def test_execution(solution: str):
    code = exec_context.replace("[insert]", solution)
    for i in range(2):
        test_input, expected_result = generate_test_case(i + 1)
        test_env = {"test_input": test_input}
        exec(code, test_env)
        assert exec_test(test_env["result"], expected_result)


Prompt:
Problem:
I have a data frame like below
    A_Name  B_Detail  Value_B  Value_C   Value_D ......
0   AA      X1        1.2      0.5       -1.3    ......
1   BB      Y1        0.76     -0.7      0.8     ......
2   CC      Z1        0.7      -1.3      2.5     ......
3   DD      L1        0.9      -0.5      0.4     ......
4   EE      M1        1.3      1.8       -1.3    ......
5   FF      N1        0.7      -0.8      0.9     ......
6   GG      K1        -2.4     -1.9      2.1     ......

This is just a sample of data frame, I can have n number of columns like (Value_A, Value_B, Value_C, ........... Value_N)
Now i want to filter all rows where absolute value of any columns (Value_A, Value_B, Value_C, ....) is more than 1 and remove 'Value_' in each column .
If you have limited number of columns, you can filter the data by simply putting 'or' condition on columns in dataframe, but I am not able to figure out what to do in this case.
I don't know what would be number of such columns, the only thing I know that such columns would be prefixed with 'Value'.
In above case output should be like
  A_Name B_Detail  B  C  D
0     AA       X1      1.2      0.5     -1.3
2     CC       Z1      0.7     -1.3      2.5
4     EE       M1      1.3      1.8     -1.3
6     GG       K1     -2.4     -1.9      2.1

A:
<code>
import pandas as pd

df = pd.DataFrame({'A_Name': ['AA', 'BB', 'CC', 'DD', 'EE', 'FF', 'GG'],
                   'B_Detail': ['X1', 'Y1', 'Z1', 'L1', 'M1', 'N1', 'K1'],
                   'Value_B': [1.2, 0.76, 0.7, 0.9, 1.3, 0.7, -2.4],
                   'Value_C': [0.5, -0.7, -1.3, -0.5, 1.8, -0.8, -1.9],
                   'Value_D': [-1.3, 0.8, 2.5, 0.4, -1.3, 0.9, 2.1]})
</code>
df = ... # put solution in this variable
BEGIN SOLUTION
<code>

def g(df):
    mask = (df.filter(like='Value').abs() > 1).any(axis=1)
    cols = {}
    for col in list(df.filter(like='Value')):
        cols[col]=col.replace("Value_","")
    df.rename(columns=cols, inplace=True)
    return df[mask]

df = g(df.copy())

import pandas as pd
import numpy as np
import copy

def generate_test_case(test_case_id):
    def generate_ans(data):
        df = data
        mask = (df.filter(like="Value").abs() > 1).any(axis=1)
        cols = {}
        for col in list(df.filter(like="Value")):
            cols[col] = col.replace("Value_", "")
        df.rename(columns=cols, inplace=True)
        return df[mask]

    def define_test_input(test_case_id):
        if test_case_id == 1:
            df = pd.DataFrame(
                {
                    "A_Name": ["AA", "BB", "CC", "DD", "EE", "FF", "GG"],
                    "B_Detail": ["X1", "Y1", "Z1", "L1", "M1", "N1", "K1"],
                    "Value_B": [1.2, 0.76, 0.7, 0.9, 1.3, 0.7, -2.4],
                    "Value_C": [0.5, -0.7, -1.3, -0.5, 1.8, -0.8, -1.9],
                    "Value_D": [-1.3, 0.8, 2.5, 0.4, -1.3, 0.9, 2.1],
                }
            )
        elif test_case_id == 2:
            df = pd.DataFrame(
                {
                    "A_Name": ["AA", "BB", "CC", "DD", "EE", "FF", "GG"],
                    "B_Detail": ["X1", "Y1", "Z1", "L1", "M1", "N1", "K1"],
                    "Value_B": [1.2, 0.76, 0.7, 0.9, 1.3, 0.7, -2.4],
                    "Value_C": [0.5, -0.7, -1.3, -0.5, 1.8, -0.8, -1.9],
                    "Value_D": [-1.3, 0.8, 2.5, 0.4, -1.3, 0.9, 2.1],
                    "Value_E": [1, 1, 4, -5, -1, -4, 2.1],
                    "Value_F": [-1.9, 2.6, 0.8, 1.7, -1.3, 0.9, 2.1],
                }
            )
        return df

    test_input = define_test_input(test_case_id)
    expected_result = generate_ans(copy.deepcopy(test_input))
    return test_input, expected_result

def exec_test(result, ans):
    try:
        pd.testing.assert_frame_equal(result, ans, check_dtype=False)
        return 1
    except:
        return 0

exec_context = r"""
import pandas as pd
import numpy as np
df = test_input
[insert]
result = df
"""

def test_execution(solution: str):
    code = exec_context.replace("[insert]", solution)
    for i in range(2):
        test_input, expected_result = generate_test_case(i + 1)
        test_env = {"test_input": test_input}
        exec(code, test_env)
        assert exec_test(test_env["result"], expected_result)


Prompt:
Problem:
In pandas, how do I replace & with '&' from all columns where &AMP could be in any position in a string?
For example, in column Title if there is a value 'Good & bad', how do I replace it with 'Good & bad'?

A:
<code>
import pandas as pd

df = pd.DataFrame({'A': ['Good & bad', 'BB', 'CC', 'DD', 'Good & bad'], 'B': range(5), 'C': ['Good & bad'] * 5})
</code>
df = ... # put solution in this variable
BEGIN SOLUTION
<code>

def g(df):
    return df.replace('&','&', regex=True)

df = g(df.copy())

import pandas as pd
import numpy as np
import copy

def generate_test_case(test_case_id):
    def generate_ans(data):
        df = data
        return df.replace("&", "&", regex=True)

    def define_test_input(test_case_id):
        if test_case_id == 1:
            df = pd.DataFrame(
                {
                    "A": ["Good & bad", "BB", "CC", "DD", "Good & bad"],
                    "B": range(5),
                    "C": ["Good & bad"] * 5,
                }
            )
        return df

    test_input = define_test_input(test_case_id)
    expected_result = generate_ans(copy.deepcopy(test_input))
    return test_input, expected_result

def exec_test(result, ans):
    try:
        pd.testing.assert_frame_equal(result, ans, check_dtype=False)
        return 1
    except:
        return 0

exec_context = r"""
import pandas as pd
import numpy as np
df = test_input
[insert]
result = df
"""

def test_execution(solution: str):
    code = exec_context.replace("[insert]", solution)
    for i in range(1):
        test_input, expected_result = generate_test_case(i + 1)
        test_env = {"test_input": test_input}
        exec(code, test_env)
        assert exec_test(test_env["result"], expected_result)


Prompt:
Problem:
In pandas, how do I replace < with '<' from all columns where &LT could be in any position in a string?
For example, in column Title if there is a value 'Good < bad', how do I replace it with 'Good < bad'?

A:
<code>
import pandas as pd

df = pd.DataFrame({'A': ['Good &LT bad', 'BB', 'CC', 'DD', 'Good < bad'], 'B': range(5), 'C': ['Good < bad'] * 5})
</code>
df = ... # put solution in this variable
BEGIN SOLUTION
<code>

def g(df):
    return df.replace('<','<', regex=True)

df = g(df.copy())

import pandas as pd
import numpy as np
import copy

def generate_test_case(test_case_id):
    def generate_ans(data):
        df = data
        return df.replace("<", "<", regex=True)

    def define_test_input(test_case_id):
        if test_case_id == 1:
            df = pd.DataFrame(
                {
                    "A": ["Good < bad", "BB", "CC", "DD", "Good < bad"],
                    "B": range(5),
                    "C": ["Good < bad"] * 5,
                }
            )
        return df

    test_input = define_test_input(test_case_id)
    expected_result = generate_ans(copy.deepcopy(test_input))
    return test_input, expected_result

def exec_test(result, ans):
    try:
        pd.testing.assert_frame_equal(result, ans, check_dtype=False)
        return 1
    except:
        return 0

exec_context = r"""
import pandas as pd
import numpy as np
df = test_input
[insert]
result = df
"""

def test_execution(solution: str):
    code = exec_context.replace("[insert]", solution)
    for i in range(1):
        test_input, expected_result = generate_test_case(i + 1)
        test_env = {"test_input": test_input}
        exec(code, test_env)
        assert exec_test(test_env["result"], expected_result)


Prompt:
Problem:
In pandas, how do I replace & with '&' from all columns where &AMP could be in any position in a string?
For example, in column Title if there is a value 'Good & bad', how do I replace it with 'Good & bad'?

A:
<code>
import pandas as pd

example_df = pd.DataFrame({'A': ['Good & bad', 'BB', 'CC', 'DD', 'Good & bad'], 'B': range(5), 'C': ['Good & bad'] * 5})
def f(df=example_df):
    # return the solution in this function
    # result = f(df)
    ### BEGIN SOLUTION

    result = df.replace('&','&', regex=True)

    return result

import pandas as pd
import numpy as np
import copy

def generate_test_case(test_case_id):
    def generate_ans(data):
        df = data
        return df.replace("&", "&", regex=True)

    def define_test_input(test_case_id):
        if test_case_id == 1:
            df = pd.DataFrame(
                {
                    "A": ["Good & bad", "BB", "CC", "DD", "Good & bad"],
                    "B": range(5),
                    "C": ["Good & bad"] * 5,
                }
            )
        return df

    test_input = define_test_input(test_case_id)
    expected_result = generate_ans(copy.deepcopy(test_input))
    return test_input, expected_result

def exec_test(result, ans):
    try:
        pd.testing.assert_frame_equal(result, ans, check_dtype=False)
        return 1
    except:
        return 0

exec_context = r"""
import pandas as pd
import numpy as np
def f(df):
[insert]
df = test_input
result = f(df)
"""

def test_execution(solution: str):
    code = exec_context.replace("[insert]", solution)
    for i in range(1):
        test_input, expected_result = generate_test_case(i + 1)
        test_env = {"test_input": test_input}
        exec(code, test_env)
        assert exec_test(test_env["result"], expected_result)


Prompt:
Problem:
In pandas, how do I replace &,<,> with '&''<''>' from all columns where &AMP could be in any position in a string?
For example, in column Title if there is a value 'Good & bad', how do I replace it with 'Good & bad'?

A:
<code>
import pandas as pd

df = pd.DataFrame({'A': ['Good & bad', 'BB', 'CC', 'DD', 'Good < bad'], 'B': range(5), 'C': ['Good > bad'] * 5})
</code>
df = ... # put solution in this variable
BEGIN SOLUTION
<code>

def g(df):
    df.replace('&', '&', regex=True, inplace=True)
    df.replace('<', '<', regex=True, inplace=True)
    df.replace('>', '>', regex=True, inplace=True)
    return df

df = g(df.copy())

import pandas as pd
import numpy as np
import copy

def generate_test_case(test_case_id):
    def generate_ans(data):
        df = data
        df.replace("&", "&", regex=True, inplace=True)
        df.replace("<", "<", regex=True, inplace=True)
        df.replace(">", ">", regex=True, inplace=True)
        return df

    def define_test_input(test_case_id):
        if test_case_id == 1:
            df = pd.DataFrame(
                {
                    "A": ["Good & bad", "BB", "CC", "DD", "Good < bad"],
                    "B": range(5),
                    "C": ["Good > bad"] * 5,
                }
            )
        return df

    test_input = define_test_input(test_case_id)
    expected_result = generate_ans(copy.deepcopy(test_input))
    return test_input, expected_result

def exec_test(result, ans):
    try:
        pd.testing.assert_frame_equal(result, ans, check_dtype=False)
        return 1
    except:
        return 0

exec_context = r"""
import pandas as pd
import numpy as np
df = test_input
[insert]
result = df
"""

def test_execution(solution: str):
    code = exec_context.replace("[insert]", solution)
    for i in range(1):
        test_input, expected_result = generate_test_case(i + 1)
        test_env = {"test_input": test_input}
        exec(code, test_env)
        assert exec_test(test_env["result"], expected_result)


Prompt:
Problem:
In pandas, how do I replace & with '&' from all columns where &AMP could be in any position in a string?Then please evaluate this expression.
For example, in column Title if there is a value '1 & 0', how do I replace it with '1 & 0 = 0'?

A:
<code>
import pandas as pd

df = pd.DataFrame({'A': ['1 & 1', 'BB', 'CC', 'DD', '1 & 0'], 'B': range(5), 'C': ['0 & 0'] * 5})
</code>
df = ... # put solution in this variable
BEGIN SOLUTION
<code>

def g(df):
    for i in df.index:
        for col in list(df):
            if type(df.loc[i, col]) == str:
                if '&' in df.loc[i, col]:
                    df.loc[i, col] = df.loc[i, col].replace('&', '&')
                    df.loc[i, col] = df.loc[i, col]+' = '+str(eval(df.loc[i, col]))
    df.replace('&', '&', regex=True)
    return df

df = g(df.copy())

import pandas as pd
import numpy as np
import copy

def generate_test_case(test_case_id):
    def generate_ans(data):
        df = data
        for i in df.index:
            for col in list(df):
                if type(df.loc[i, col]) == str:
                    if "&" in df.loc[i, col]:
                        df.loc[i, col] = df.loc[i, col].replace("&", "&")
                        df.loc[i, col] = (
                            df.loc[i, col] + " = " + str(eval(df.loc[i, col]))
                        )
        df.replace("&", "&", regex=True)
        return df

    def define_test_input(test_case_id):
        if test_case_id == 1:
            df = pd.DataFrame(
                {
                    "A": ["1 & 1", "BB", "CC", "DD", "1 & 0"],
                    "B": range(5),
                    "C": ["0 & 0"] * 5,
                }
            )
        return df

    test_input = define_test_input(test_case_id)
    expected_result = generate_ans(copy.deepcopy(test_input))
    return test_input, expected_result

def exec_test(result, ans):
    try:
        pd.testing.assert_frame_equal(result, ans, check_dtype=False)
        return 1
    except:
        return 0

exec_context = r"""
import pandas as pd
import numpy as np
df = test_input
[insert]
result = df
"""

def test_execution(solution: str):
    code = exec_context.replace("[insert]", solution)
    for i in range(1):
        test_input, expected_result = generate_test_case(i + 1)
        test_env = {"test_input": test_input}
        exec(code, test_env)
        assert exec_test(test_env["result"], expected_result)


Prompt:
Problem:
Let's say I have a pandas DataFrame containing names like so:
name_df = pd.DataFrame({'name':['Jack Fine','Kim Q. Danger','Jane Smith', 'Juan de la Cruz']})
    name
0   Jack Fine
1   Kim Q. Danger
2   Jane Smith
3   Juan de la Cruz

and I want to split the name column into first_name and last_name IF there is one space in the name. Otherwise I want the full name to be shoved into first_name.
So the final DataFrame should look like:
  first_name     last_name
0 Jack           Fine
1 Kim Q. Danger           None
2 Jane           Smith
3 Juan de la Cruz           None

I've tried to accomplish this by first applying the following function to return names that can be split into first and last name:
def validate_single_space_name(name: str) -> str:
    pattern = re.compile(r'^.*( ){1}.*$')
    match_obj = re.match(pattern, name)
    if match_obj:
        return name
    else:
        return None

However applying this function to my original name_df, leads to an empty DataFrame, not one populated by names that can be split and Nones.
Help getting my current approach to work, or solutions invovling a different approach would be appreciated!

A:
<code>
import pandas as pd

df = pd.DataFrame({'name':['Jack Fine','Kim Q. Danger','Jane Smith', 'Zhongli']})
</code>
df = ... # put solution in this variable
BEGIN SOLUTION
<code>

def g(df):
    df.loc[df['name'].str.split().str.len() == 2, 'last_name'] = df['name'].str.split().str[-1]
    df.loc[df['name'].str.split().str.len() == 2, 'name'] = df['name'].str.split().str[0]
    df.rename(columns={'name': 'first_name'}, inplace=True)
    return df

df = g(df.copy())

import pandas as pd
import numpy as np
import copy

def generate_test_case(test_case_id):
    def generate_ans(data):
        df = data
        df.loc[df["name"].str.split().str.len() == 2, "last_name"] = (
            df["name"].str.split().str[-1]
        )
        df.loc[df["name"].str.split().str.len() == 2, "name"] = (
            df["name"].str.split().str[0]
        )
        df.rename(columns={"name": "first_name"}, inplace=True)
        return df

    def define_test_input(test_case_id):
        if test_case_id == 1:
            df = pd.DataFrame(
                {"name": ["Jack Fine", "Kim Q. Danger", "Jane Smith", "Zhongli"]}
            )
        return df

    test_input = define_test_input(test_case_id)
    expected_result = generate_ans(copy.deepcopy(test_input))
    return test_input, expected_result

def exec_test(result, ans):
    try:
        pd.testing.assert_frame_equal(result, ans, check_dtype=False)
        return 1
    except:
        return 0

exec_context = r"""
import pandas as pd
import numpy as np
df = test_input
[insert]
result = df
"""

def test_execution(solution: str):
    code = exec_context.replace("[insert]", solution)
    for i in range(1):
        test_input, expected_result = generate_test_case(i + 1)
        test_env = {"test_input": test_input}
        exec(code, test_env)
        assert exec_test(test_env["result"], expected_result)


Prompt:
Problem:
Let's say I have a pandas DataFrame containing names like so:
name_df = pd.DataFrame({'name':['Jack Fine','Kim Q. Danger','Jane Smith', 'Juan de la Cruz']})
    name
0   Jack Fine
1   Kim Q. Danger
2   Jane Smith
3   Juan de la Cruz

and I want to split the name column into 1_name and 2_name IF there is one space in the name. Otherwise I want the full name to be shoved into 1_name.
So the final DataFrame should look like:
  1_name     2_name
0 Jack           Fine
1 Kim Q. Danger
2 Jane           Smith
3 Juan de la Cruz

I've tried to accomplish this by first applying the following function to return names that can be split into first and last name:
def validate_single_space_name(name: str) -> str:
    pattern = re.compile(r'^.*( ){1}.*$')
    match_obj = re.match(pattern, name)
    if match_obj:
        return name
    else:
        return None

However applying this function to my original name_df, leads to an empty DataFrame, not one populated by names that can be split and Nones.
Help getting my current approach to work, or solutions invovling a different approach would be appreciated!

A:
<code>
import pandas as pd

df = pd.DataFrame({'name':['Jack Fine','Kim Q. Danger','Jane Smith', 'Zhongli']})
</code>
df = ... # put solution in this variable
BEGIN SOLUTION
<code>

def g(df):
    df.loc[df['name'].str.split().str.len() == 2, '2_name'] = df['name'].str.split().str[-1]
    df.loc[df['name'].str.split().str.len() == 2, 'name'] = df['name'].str.split().str[0]
    df.rename(columns={'name': '1_name'}, inplace=True)
    return df

df = g(df.copy())

import pandas as pd
import numpy as np
import copy

def generate_test_case(test_case_id):
    def generate_ans(data):
        df = data
        df.loc[df["name"].str.split().str.len() == 2, "2_name"] = (
            df["name"].str.split().str[-1]
        )
        df.loc[df["name"].str.split().str.len() == 2, "name"] = (
            df["name"].str.split().str[0]
        )
        df.rename(columns={"name": "1_name"}, inplace=True)
        return df

    def define_test_input(test_case_id):
        if test_case_id == 1:
            df = pd.DataFrame(
                {"name": ["Jack Fine", "Kim Q. Danger", "Jane Smith", "Zhongli"]}
            )
        return df

    test_input = define_test_input(test_case_id)
    expected_result = generate_ans(copy.deepcopy(test_input))
    return test_input, expected_result

def exec_test(result, ans):
    try:
        pd.testing.assert_frame_equal(result, ans, check_dtype=False)
        return 1
    except:
        return 0

exec_context = r"""
import pandas as pd
import numpy as np
df = test_input
[insert]
result = df
"""

def test_execution(solution: str):
    code = exec_context.replace("[insert]", solution)
    for i in range(1):
        test_input, expected_result = generate_test_case(i + 1)
        test_env = {"test_input": test_input}
        exec(code, test_env)
        assert exec_test(test_env["result"], expected_result)


Prompt:
Problem:
Let's say I have a pandas DataFrame containing names like so:
name_df = pd.DataFrame({'name':['Jack Fine','Kim Q. Danger','Jane Smith', 'Juan de la Cruz']})
                 name
0           Jack Fine
1       Kim Q. Danger
2  Jane 114 514 Smith
3             Zhongli

and I want to split the name column into first_name, middle_name and last_name IF there is more than one space in the name.
So the final DataFrame should look like:
  first name middle_name last_name
0       Jack         NaN      Fine
1        Kim          Q.    Danger
2       Jane     114 514     Smith
3    Zhongli         NaN       NaN

I've tried to accomplish this by first applying the following function to return names that can be split into first and last name:
def validate_single_space_name(name: str) -> str:
    pattern = re.compile(r'^.*( ){1}.*$')
    match_obj = re.match(pattern, name)
    if match_obj:
        return name
    else:
        return None

However applying this function to my original name_df, leads to an empty DataFrame, not one populated by names that can be split and Nones.
Help getting my current approach to work, or solutions invovling a different approach would be appreciated!

A:
<code>
import pandas as pd

df = pd.DataFrame({'name':['Jack Fine','Kim Q. Danger','Jane 114 514 Smith', 'Zhongli']})
</code>
df = ... # put solution in this variable
BEGIN SOLUTION
<code>

def g(df):
    df.loc[df['name'].str.split().str.len() >= 3, 'middle_name'] = df['name'].str.split().str[1:-1]
    for i in range(len(df)):
        if len(df.loc[i, 'name'].split()) >= 3:
            l = df.loc[i, 'name'].split()[1:-1]
            s = l[0]
            for j in range(1,len(l)):
                s += ' '+l[j]
            df.loc[i, 'middle_name'] = s
    df.loc[df['name'].str.split().str.len() >= 2, 'last_name'] = df['name'].str.split().str[-1]
    df.loc[df['name'].str.split().str.len() >= 2, 'name'] = df['name'].str.split().str[0]
    df.rename(columns={'name': 'first name'}, inplace=True)
    return df

df = g(df.copy())

import pandas as pd
import numpy as np
import copy

def generate_test_case(test_case_id):
    def generate_ans(data):
        df = data
        df.loc[df["name"].str.split().str.len() >= 3, "middle_name"] = (
            df["name"].str.split().str[1:-1]
        )
        for i in range(len(df)):
            if len(df.loc[i, "name"].split()) >= 3:
                l = df.loc[i, "name"].split()[1:-1]
                s = l[0]
                for j in range(1, len(l)):
                    s += " " + l[j]
                df.loc[i, "middle_name"] = s
        df.loc[df["name"].str.split().str.len() >= 2, "last_name"] = (
            df["name"].str.split().str[-1]
        )
        df.loc[df["name"].str.split().str.len() >= 2, "name"] = (
            df["name"].str.split().str[0]
        )
        df.rename(columns={"name": "first name"}, inplace=True)
        return df

    def define_test_input(test_case_id):
        if test_case_id == 1:
            df = pd.DataFrame(
                {
                    "name": [
                        "Jack Fine",
                        "Kim Q. Danger",
                        "Jane 114 514 Smith",
                        "Zhongli",
                    ]
                }
            )
        return df

    test_input = define_test_input(test_case_id)
    expected_result = generate_ans(copy.deepcopy(test_input))
    return test_input, expected_result

def exec_test(result, ans):
    try:
        pd.testing.assert_frame_equal(result, ans, check_dtype=False)
        return 1
    except:
        return 0

exec_context = r"""
import pandas as pd
import numpy as np
df = test_input
[insert]
result = df
"""

def test_execution(solution: str):
    code = exec_context.replace("[insert]", solution)
    for i in range(1):
        test_input, expected_result = generate_test_case(i + 1)
        test_env = {"test_input": test_input}
        exec(code, test_env)
        assert exec_test(test_env["result"], expected_result)


Prompt:
Problem:
Say I have two dataframes:
df1:                          df2:
+-------------------+----+    +-------------------+-----+
|  Timestamp        |data|    |  Timestamp        |stuff|
+-------------------+----+    +-------------------+-----+
|2019/04/02 11:00:01| 111|    |2019/04/02 11:00:14|  101|
|2019/04/02 11:00:15| 222|    |2019/04/02 11:00:15|  202|
|2019/04/02 11:00:29| 333|    |2019/04/02 11:00:16|  303|
|2019/04/02 11:00:30| 444|    |2019/04/02 11:00:30|  404|
+-------------------+----+    |2019/04/02 11:00:31|  505|
                              +-------------------+-----+

Without looping through every row of df2, I am trying to join the two dataframes based on the timestamp. So for every row in df2, it will "add" data from df1 that was at that particular time. In this example, the resulting dataframe would be:
Adding df1 data to df2:
+-------------------+-----+----+
|  Timestamp        |stuff|data|
+-------------------+-----+----+
|2019/04/02 11:00:14|  101| 222|
|2019/04/02 11:00:15|  202| 222|
|2019/04/02 11:00:16|  303| 333|
|2019/04/02 11:00:30|  404| 444|
|2019/04/02 11:00:31|  505|None|
+-------------------+-----+----+

Looping through each row of df2 then comparing to each df1 is very inefficient. Is there another way?

A:
<code>
import pandas as pd

df1 = pd.DataFrame({'Timestamp': ['2019/04/02 11:00:01', '2019/04/02 11:00:15', '2019/04/02 11:00:29', '2019/04/02 11:00:30'],
                    'data': [111, 222, 333, 444]})
df2 = pd.DataFrame({'Timestamp': ['2019/04/02 11:00:14', '2019/04/02 11:00:15', '2019/04/02 11:00:16', '2019/04/02 11:00:30', '2019/04/02 11:00:31'],
                    'stuff': [101, 202, 303, 404, 505]})
df1['Timestamp'] = pd.to_datetime(df1['Timestamp'])
df2['Timestamp'] = pd.to_datetime(df2['Timestamp'])
</code>
result = ... # put solution in this variable
BEGIN SOLUTION
<code>

def g(df1, df2):
    return pd.merge_asof(df2, df1, on='Timestamp', direction='forward')

result = g(df1.copy(), df2.copy())


Prompt:
Problem:
Say I have two dataframes:
df1:                          df2:
+-------------------+----+    +-------------------+-----+
|  Timestamp        |data|    |  Timestamp        |stuff|
+-------------------+----+    +-------------------+-----+
|2019/04/02 11:00:01| 111|    |2019/04/02 11:00:14|  101|
|2019/04/02 11:00:15| 222|    |2019/04/02 11:00:15|  202|
|2019/04/02 11:00:29| 333|    |2019/04/02 11:00:16|  303|
|2019/04/02 11:00:30| 444|    |2019/04/02 11:00:30|  404|
+-------------------+----+    |2019/04/02 11:00:31|  505|
                              +-------------------+-----+

Without looping through every row of df1, I am trying to join the two dataframes based on the timestamp. So for every row in df1, it will "add" data from df2 that was at that particular time. In this example, the resulting dataframe would be:
Adding df1 data to df2:
            Timestamp  data  stuff
0 2019-04-02 11:00:01   111    101
1 2019-04-02 11:00:15   222    202
2 2019-04-02 11:00:29   333    404
3 2019-04-02 11:00:30   444    404

Looping through each row of df1 then comparing to each df2 is very inefficient. Is there another way?

A:
<code>
import pandas as pd

df1 = pd.DataFrame({'Timestamp': ['2019/04/02 11:00:01', '2019/04/02 11:00:15', '2019/04/02 11:00:29', '2019/04/02 11:00:30'],
                    'data': [111, 222, 333, 444]})

df2 = pd.DataFrame({'Timestamp': ['2019/04/02 11:00:14', '2019/04/02 11:00:15', '2019/04/02 11:00:16', '2019/04/02 11:00:30', '2019/04/02 11:00:31'],
                    'stuff': [101, 202, 303, 404, 505]})

df1['Timestamp'] = pd.to_datetime(df1['Timestamp'])
df2['Timestamp'] = pd.to_datetime(df2['Timestamp'])
</code>
result = ... # put solution in this variable
BEGIN SOLUTION
<code>

def g(df1, df2):
    return pd.merge_asof(df1, df2, on='Timestamp', direction='forward')

result = g(df1.copy(), df2.copy())


Prompt:
Problem:
I have an example data as:
datetime             col1    col2    col3
2021-04-10 01:00:00    25.    50.     50
2021-04-10 02:00:00.   25.    50.     50
2021-04-10 03:00:00.   25.    100.    50
2021-04-10 04:00:00    50.     50.    100
2021-04-10 05:00:00.   100.    100.   100

I want to create a new column called state, which returns col1 value if col2 and col3 values are  less than or equal to 50 otherwise returns the max value between col1,column2 and column3.
The expected output is as shown below:
datetime             col1    col2    col3. state
2021-04-10 01:00:00    25.    50.     50.   25
2021-04-10 02:00:00.   25.    50.     50.   25
2021-04-10 03:00:00.   25.    100.    50.   100
2021-04-10 04:00:00    50.     50.    100.  100
2021-04-10 05:00:00.   100.    100.   100.  100

A:
<code>
import pandas as pd

df = pd.DataFrame({'datetime': ['2021-04-10 01:00:00', '2021-04-10 02:00:00', '2021-04-10 03:00:00', '2021-04-10 04:00:00', '2021-04-10 05:00:00'],
                   'col1': [25, 25, 25, 50, 100],
                   'col2': [50, 50, 100, 50, 100],
                   'col3': [50, 50, 50, 100, 100]})
df['datetime'] = pd.to_datetime(df['datetime'])
</code>
df = ... # put solution in this variable
BEGIN SOLUTION
<code>

import numpy as np
def g(df):
    df['state'] = np.where((df['col2'] <= 50) & (df['col3'] <= 50), df['col1'], df[['col1', 'col2', 'col3']].max(axis=1))
    return df

df = g(df.copy())

import pandas as pd
import numpy as np
import copy

def generate_test_case(test_case_id):
    def generate_ans(data):
        df = data
        df["state"] = np.where(
            (df["col2"] <= 50) & (df["col3"] <= 50),
            df["col1"],
            df[["col1", "col2", "col3"]].max(axis=1),
        )
        return df

    def define_test_input(test_case_id):
        if test_case_id == 1:
            df = pd.DataFrame(
                {
                    "datetime": [
                        "2021-04-10 01:00:00",
                        "2021-04-10 02:00:00",
                        "2021-04-10 03:00:00",
                        "2021-04-10 04:00:00",
                        "2021-04-10 05:00:00",
                    ],
                    "col1": [25, 25, 25, 50, 100],
                    "col2": [50, 50, 100, 50, 100],
                    "col3": [50, 50, 50, 100, 100],
                }
            )
            df["datetime"] = pd.to_datetime(df["datetime"])
        elif test_case_id == 2:
            df = pd.DataFrame(
                {
                    "datetime": [
                        "2021-04-10 01:00:00",
                        "2021-04-10 02:00:00",
                        "2021-04-10 03:00:00",
                        "2021-04-10 04:00:00",
                        "2021-04-10 05:00:00",
                    ],
                    "col1": [50, 25, 25, 50, 100],
                    "col2": [50, 50, 10, 50, 100],
                    "col3": [50, 50, 13, 100, 100],
                }
            )
            df["datetime"] = pd.to_datetime(df["datetime"])
        return df

    test_input = define_test_input(test_case_id)
    expected_result = generate_ans(copy.deepcopy(test_input))
    return test_input, expected_result

def exec_test(result, ans):
    try:
        pd.testing.assert_frame_equal(result, ans, check_dtype=False)
        return 1
    except:
        return 0

exec_context = r"""
import pandas as pd
df = test_input
[insert]
result = df
"""

def test_execution(solution: str):
    code = exec_context.replace("[insert]", solution)
    for i in range(2):
        test_input, expected_result = generate_test_case(i + 1)
        test_env = {"test_input": test_input}
        exec(code, test_env)
        assert exec_test(test_env["result"], expected_result)


Prompt:
Problem:
I have an example data as:
datetime             col1    col2    col3
2021-04-10 01:00:00    25.    50.     50
2021-04-10 02:00:00.   25.    50.     50
2021-04-10 03:00:00.   25.    100.    50
2021-04-10 04:00:00    50.     50.    100
2021-04-10 05:00:00.   100.    100.   100

I want to create a new column called state, which returns col1 value if col2 and col3 values are  more than 50 otherwise returns the sum value of col1,column2 and column3.
The expected output is as shown below:
             datetime  col1  col2  col3  state
0 2021-04-10 01:00:00    25    50    50    125
1 2021-04-10 02:00:00    25    50    50    125
2 2021-04-10 03:00:00    25   100    50    175
3 2021-04-10 04:00:00    50    50   100    200
4 2021-04-10 05:00:00   100   100   100    100

A:
<code>
import pandas as pd

df = pd.DataFrame({'datetime': ['2021-04-10 01:00:00', '2021-04-10 02:00:00', '2021-04-10 03:00:00', '2021-04-10 04:00:00', '2021-04-10 05:00:00'],
                   'col1': [25, 25, 25, 50, 100],
                   'col2': [50, 50, 100, 50, 100],
                   'col3': [50, 50, 50, 100, 100]})

df['datetime'] = pd.to_datetime(df['datetime'])
</code>
df = ... # put solution in this variable
BEGIN SOLUTION
<code>

import numpy as np
def g(df):
    df['state'] = np.where((df['col2'] > 50) & (df['col3'] > 50), df['col1'], df[['col1', 'col2', 'col3']].sum(axis=1))
    return df

df = g(df.copy())

import pandas as pd
import numpy as np
import copy

def generate_test_case(test_case_id):
    def generate_ans(data):
        df = data
        df["state"] = np.where(
            (df["col2"] > 50) & (df["col3"] > 50),
            df["col1"],
            df[["col1", "col2", "col3"]].sum(axis=1),
        )
        return df

    def define_test_input(test_case_id):
        if test_case_id == 1:
            df = pd.DataFrame(
                {
                    "datetime": [
                        "2021-04-10 01:00:00",
                        "2021-04-10 02:00:00",
                        "2021-04-10 03:00:00",
                        "2021-04-10 04:00:00",
                        "2021-04-10 05:00:00",
                    ],
                    "col1": [25, 25, 25, 50, 100],
                    "col2": [50, 50, 100, 50, 100],
                    "col3": [50, 50, 50, 100, 100],
                }
            )
            df["datetime"] = pd.to_datetime(df["datetime"])
        elif test_case_id == 2:
            df = pd.DataFrame(
                {
                    "datetime": [
                        "2021-04-10 01:00:00",
                        "2021-04-10 02:00:00",
                        "2021-04-10 03:00:00",
                        "2021-04-10 04:00:00",
                        "2021-04-10 05:00:00",
                    ],
                    "col1": [25, 10, 66, 50, 100],
                    "col2": [50, 13, 100, 50, 100],
                    "col3": [50, 16, 50, 100, 100],
                }
            )
            df["datetime"] = pd.to_datetime(df["datetime"])
        return df

    test_input = define_test_input(test_case_id)
    expected_result = generate_ans(copy.deepcopy(test_input))
    return test_input, expected_result

def exec_test(result, ans):
    try:
        pd.testing.assert_frame_equal(result, ans, check_dtype=False)
        return 1
    except:
        return 0

exec_context = r"""
import pandas as pd
df = test_input
[insert]
result = df
"""

def test_execution(solution: str):
    code = exec_context.replace("[insert]", solution)
    for i in range(2):
        test_input, expected_result = generate_test_case(i + 1)
        test_env = {"test_input": test_input}
        exec(code, test_env)
        assert exec_test(test_env["result"], expected_result)


Prompt:
Problem:
I have a pandas dataframe with a column which could have integers, float, string etc. I would like to iterate over all the rows and check if each value is integer and if not, I would like to create a list with error values (values that are not integer)
I have tried isnumeric(), but couldnt iterate over each row and write errors to output. I tried using iterrows() but it converts all values to float.
ID     Field1
1      1.15
2      2
3      1
4      25
5      and

Expected Result:
[1.15,"and"]

A:
<code>
import pandas as pd

df = pd.DataFrame({"ID": [1,2,3,4,5], "Field1": [1.15,2,1,25,"and"]})
</code>
df = ... # put solution in this variable
BEGIN SOLUTION
<code>

def g(df):
    return df.loc[~df['Field1'].astype(str).str.isdigit(), 'Field1'].tolist()

df = g(df.copy())

import pandas as pd
import numpy as np
import copy

def generate_test_case(test_case_id):
    def generate_ans(data):
        df = data
        return df.loc[~df["Field1"].astype(str).str.isdigit(), "Field1"].tolist()

    def define_test_input(test_case_id):
        if test_case_id == 1:
            df = pd.DataFrame(
                {"ID": [1, 2, 3, 4, 5], "Field1": [1.15, 2, 1, 25, "and"]}
            )
        if test_case_id == 2:
            df = pd.DataFrame({"ID": [1, 2, 3, 4, 5], "Field1": [1, 2, 1, 2.5, "and"]})
        return df

    test_input = define_test_input(test_case_id)
    expected_result = generate_ans(copy.deepcopy(test_input))
    return test_input, expected_result

def exec_test(result, ans):
    try:
        assert result == ans
        return 1
    except:
        return 0

exec_context = r"""
import pandas as pd
import numpy as np
df = test_input
[insert]
result = df
"""

def test_execution(solution: str):
    code = exec_context.replace("[insert]", solution)
    for i in range(2):
        test_input, expected_result = generate_test_case(i + 1)
        test_env = {"test_input": test_input}
        exec(code, test_env)
        assert exec_test(test_env["result"], expected_result)


Prompt:
Problem:
I have a pandas dataframe with a column which could have integers, float, string etc. I would like to iterate over all the rows and check if each value is integer and if not, I would like to create a list with integer values
I have tried isnumeric(), but couldnt iterate over each row and write errors to output. I tried using iterrows() but it converts all values to float.
ID     Field1
1      1.15
2      2
3      1
4      25
5      and

Expected Result:
[2, 1, 25]

A:
<code>
import pandas as pd

df = pd.DataFrame({"ID": [1,2,3,4,5], "Field1": [1.15,2,1,25,"and"]})
</code>
df = ... # put solution in this variable
BEGIN SOLUTION
<code>

def g(df):
    return df.loc[df['Field1'].astype(str).str.isdigit(), 'Field1'].tolist()

df = g(df.copy())

import pandas as pd
import numpy as np
import copy

def generate_test_case(test_case_id):
    def generate_ans(data):
        df = data
        return df.loc[df["Field1"].astype(str).str.isdigit(), "Field1"].tolist()

    def define_test_input(test_case_id):
        if test_case_id == 1:
            df = pd.DataFrame(
                {"ID": [1, 2, 3, 4, 5], "Field1": [1.15, 2, 1, 25, "and"]}
            )
        if test_case_id == 2:
            df = pd.DataFrame({"ID": [1, 2, 3, 4, 5], "Field1": [1, 2, 1, 2.5, "and"]})
        return df

    test_input = define_test_input(test_case_id)
    expected_result = generate_ans(copy.deepcopy(test_input))
    return test_input, expected_result

def exec_test(result, ans):
    try:
        assert result == ans
        return 1
    except:
        return 0

exec_context = r"""
import pandas as pd
import numpy as np
df = test_input
[insert]
result = df
"""

def test_execution(solution: str):
    code = exec_context.replace("[insert]", solution)
    for i in range(2):
        test_input, expected_result = generate_test_case(i + 1)
        test_env = {"test_input": test_input}
        exec(code, test_env)
        assert exec_test(test_env["result"], expected_result)


Prompt:
Problem:
I have a pandas dataframe with a column which could have integers, float, string etc. I would like to iterate over all the rows and check if each value is integer and if not, I would like to create a list with error values (values that are not integer)
I have tried isnumeric(), but couldnt iterate over each row and write errors to output. I tried using iterrows() but it converts all values to float.
ID     Field1
1      1.15
2      2
3      1
4      25
5      and

Expected Result:
[1.15,"and"]

A:
<code>
import pandas as pd

example_df = pd.DataFrame({"ID": [1,2,3,4,5], "Field1": [1.15,2,1,25,"and"]})
def f(df=example_df):
    # return the solution in this function
    # result = f(df)
    ### BEGIN SOLUTION

    result = df.loc[~df['Field1'].astype(str).str.isdigit(), 'Field1'].tolist()

    return result

import pandas as pd
import numpy as np
import copy

def generate_test_case(test_case_id):
    def generate_ans(data):
        df = data
        return df.loc[~df["Field1"].astype(str).str.isdigit(), "Field1"].tolist()

    def define_test_input(test_case_id):
        if test_case_id == 1:
            df = pd.DataFrame(
                {"ID": [1, 2, 3, 4, 5], "Field1": [1.15, 2, 1, 25, "and"]}
            )
        if test_case_id == 2:
            df = pd.DataFrame({"ID": [1, 2, 3, 4, 5], "Field1": [1, 2, 1, 2.5, "and"]})
        return df

    test_input = define_test_input(test_case_id)
    expected_result = generate_ans(copy.deepcopy(test_input))
    return test_input, expected_result

def exec_test(result, ans):
    try:
        assert result == ans
        return 1
    except:
        return 0

exec_context = r"""
import pandas as pd
import numpy as np
def f(df):
[insert]
df = test_input
result = f(df)
"""

def test_execution(solution: str):
    code = exec_context.replace("[insert]", solution)
    for i in range(2):
        test_input, expected_result = generate_test_case(i + 1)
        test_env = {"test_input": test_input}
        exec(code, test_env)
        assert exec_test(test_env["result"], expected_result)


Prompt:
Problem:
I have my data in a pandas DataFrame, and it looks like the following:
cat  val1   val2   val3   val4
A    7      10     0      19
B    10     2      1      14
C    5      15     6      16

I'd like to compute the percentage of the category (cat) that each value has.
For example, for category A, val1 is 7 and the row total is 36. The resulting value would be 7/36, so val1 is 19.4% of category A.
My expected result would look like the following:
cat  val1   val2   val3   val4
A    .194   .278   .0     .528
B    .370   .074   .037   .519
C    .119   .357   .143   .381

Is there an easy way to compute this?

A:
<code>
import pandas as pd

df = pd.DataFrame({'cat': ['A', 'B', 'C'],
                   'val1': [7, 10, 5],
                   'val2': [10, 2, 15],
                   'val3': [0, 1, 6],
                   'val4': [19, 14, 16]})
</code>
df = ... # put solution in this variable
BEGIN SOLUTION
<code>

def g(df):
    df = df.set_index('cat')
    res = df.div(df.sum(axis=1), axis=0)
    return res.reset_index()

df = g(df.copy())

import pandas as pd
import numpy as np
import copy

def generate_test_case(test_case_id):
    def generate_ans(data):
        df = data
        df = df.set_index("cat")
        res = df.div(df.sum(axis=1), axis=0)
        return res.reset_index()

    def define_test_input(test_case_id):
        if test_case_id == 1:
            df = pd.DataFrame(
                {
                    "cat": ["A", "B", "C"],
                    "val1": [7, 10, 5],
                    "val2": [10, 2, 15],
                    "val3": [0, 1, 6],
                    "val4": [19, 14, 16],
                }
            )
        if test_case_id == 2:
            df = pd.DataFrame(
                {
                    "cat": ["A", "B", "C"],
                    "val1": [1, 1, 4],
                    "val2": [10, 2, 15],
                    "val3": [0, 1, 6],
                    "val4": [19, 14, 16],
                }
            )
        return df

    test_input = define_test_input(test_case_id)
    expected_result = generate_ans(copy.deepcopy(test_input))
    return test_input, expected_result

def exec_test(result, ans):
    try:
        pd.testing.assert_frame_equal(result, ans, check_dtype=False)
        return 1
    except:
        return 0

exec_context = r"""
import pandas as pd
import numpy as np
df = test_input
[insert]
result = df
"""

def test_execution(solution: str):
    code = exec_context.replace("[insert]", solution)
    for i in range(2):
        test_input, expected_result = generate_test_case(i + 1)
        test_env = {"test_input": test_input}
        exec(code, test_env)
        assert exec_test(test_env["result"], expected_result)


Prompt:
Problem:
I have my data in a pandas DataFrame, and it looks like the following:
cat  val1   val2   val3   val4
A    7      10     0      19
B    10     2      1      14
C    5      15     6      16

I'd like to compute the percentage of the value that each category(cat) has.
For example, for val1, A is 7 and the column total is 22. The resulting value would be 7/22, so A is 31.8% of val1.
My expected result would look like the following:
  cat      val1      val2      val3      val4
0   A  0.318182  0.370370  0.000000  0.387755
1   B  0.454545  0.074074  0.142857  0.285714
2   C  0.227273  0.555556  0.857143  0.326531

Is there an easy way to compute this?

A:
<code>
import pandas as pd

df = pd.DataFrame({'cat': ['A', 'B', 'C'],
                   'val1': [7, 10, 5],
                   'val2': [10, 2, 15],
                   'val3': [0, 1, 6],
                   'val4': [19, 14, 16]})
</code>
df = ... # put solution in this variable
BEGIN SOLUTION
<code>

def g(df):
    df = df.set_index('cat')
    res = df.div(df.sum(axis=0), axis=1)
    return res.reset_index()

df = g(df.copy())

import pandas as pd
import numpy as np
import copy

def generate_test_case(test_case_id):
    def generate_ans(data):
        df = data
        df = df.set_index("cat")
        res = df.div(df.sum(axis=0), axis=1)
        return res.reset_index()

    def define_test_input(test_case_id):
        if test_case_id == 1:
            df = pd.DataFrame(
                {
                    "cat": ["A", "B", "C"],
                    "val1": [7, 10, 5],
                    "val2": [10, 2, 15],
                    "val3": [0, 1, 6],
                    "val4": [19, 14, 16],
                }
            )
        if test_case_id == 2:
            df = pd.DataFrame(
                {
                    "cat": ["A", "B", "C"],
                    "val1": [1, 1, 4],
                    "val2": [10, 2, 15],
                    "val3": [0, 1, 6],
                    "val4": [19, 14, 16],
                }
            )
        return df

    test_input = define_test_input(test_case_id)
    expected_result = generate_ans(copy.deepcopy(test_input))
    return test_input, expected_result

def exec_test(result, ans):
    try:
        pd.testing.assert_frame_equal(result, ans, check_dtype=False)
        return 1
    except:
        return 0

exec_context = r"""
import pandas as pd
import numpy as np
df = test_input
[insert]
result = df
"""

def test_execution(solution: str):
    code = exec_context.replace("[insert]", solution)
    for i in range(2):
        test_input, expected_result = generate_test_case(i + 1)
        test_env = {"test_input": test_input}
        exec(code, test_env)
        assert exec_test(test_env["result"], expected_result)


Prompt:
Problem:
I am trying to extract rows from a Pandas dataframe using a list of row names, but it can't be done. Here is an example

# df
    alleles  chrom  pos strand  assembly#  center  protLSID  assayLSID
rs#
TP3      A/C      0    3      +        NaN     NaN       NaN        NaN
TP7      A/T      0    7      +        NaN     NaN       NaN        NaN
TP12     T/A      0   12      +        NaN     NaN       NaN        NaN
TP15     C/A      0   15      +        NaN     NaN       NaN        NaN
TP18     C/T      0   18      +        NaN     NaN       NaN        NaN

test = ['TP3','TP12','TP18']

df.select(test)
This is what I was trying to do with just element of the list and I am getting this error TypeError: 'Index' object is not callable. What am I doing wrong?

A:
<code>
import pandas as pd
import io

data = io.StringIO("""
rs  alleles  chrom  pos strand  assembly#  center  protLSID  assayLSID
TP3      A/C      0    3      +        NaN     NaN       NaN        NaN
TP7      A/T      0    7      +        NaN     NaN       NaN        NaN
TP12     T/A      0   12      +        NaN     NaN       NaN        NaN
TP15     C/A      0   15      +        NaN     NaN       NaN        NaN
TP18     C/T      0   18      +        NaN     NaN       NaN        NaN
""")
df = pd.read_csv(data, delim_whitespace=True).set_index('rs')
test = ['TP3', 'TP7', 'TP18']
</code>
result = ... # put solution in this variable
BEGIN SOLUTION
<code>

def g(df, test):
    return df.loc[test]

result = g(df, test)

import pandas as pd
import numpy as np
import os
import io
import copy

def generate_test_case(test_case_id):
    def generate_ans(data):
        data = data
        df, test = data
        return df.loc[test]

    def define_test_input(test_case_id):
        if test_case_id == 1:
            data = io.StringIO(
                """
            rs  alleles  chrom  pos strand  assembly#  center  protLSID  assayLSID
            TP3      A/C      0    3      +        NaN     NaN       NaN        NaN
            TP7      A/T      0    7      +        NaN     NaN       NaN        NaN
            TP12     T/A      0   12      +        NaN     NaN       NaN        NaN
            TP15     C/A      0   15      +        NaN     NaN       NaN        NaN
            TP18     C/T      0   18      +        NaN     NaN       NaN        NaN
            """
            )
            df = pd.read_csv(data, delim_whitespace=True).set_index("rs")
            test = ["TP3", "TP7", "TP18"]
        return df, test

    test_input = define_test_input(test_case_id)
    expected_result = generate_ans(copy.deepcopy(test_input))
    return test_input, expected_result

def exec_test(result, ans):
    try:
        pd.testing.assert_frame_equal(result, ans, check_dtype=False)
        return 1
    except:
        return 0

exec_context = r"""
import pandas as pd
import numpy as np
import io
df, test = test_input
[insert]
"""

def test_execution(solution: str):
    code = exec_context.replace("[insert]", solution)
    for i in range(1):
        test_input, expected_result = generate_test_case(i + 1)
        test_env = {"test_input": test_input}
        exec(code, test_env)
        assert exec_test(test_env["result"], expected_result)


Prompt:
Problem:
I am trying to extract rows from a Pandas dataframe using a list of row names, but it can't be done. Here is an example

# df
    alias  chrome  poston
rs#
TP3      A/C      0    3
TP7      A/T      0    7
TP12     T/A      0   12
TP15     C/A      0   15
TP18     C/T      0   18

rows = ['TP3', 'TP18']

df.select(rows)
This is what I was trying to do with just element of the list and I am getting this error TypeError: 'Index' object is not callable. What am I doing wrong?

A:
<code>
import pandas as pd
import io

data = io.StringIO("""
rs    alias  chrome  poston
TP3      A/C      0    3
TP7      A/T      0    7
TP12     T/A      0   12
TP15     C/A      0   15
TP18     C/T      0   18
""")
df = pd.read_csv(data, delim_whitespace=True).set_index('rs')
test = ['TP3', 'TP18']
</code>
result = ... # put solution in this variable
BEGIN SOLUTION
<code>

def g(df, test):
    return df.loc[test]

result = g(df, test)

import pandas as pd
import numpy as np
import io
import copy

def generate_test_case(test_case_id):
    def generate_ans(data):
        data = data
        df, test = data
        return df.loc[test]

    def define_test_input(test_case_id):
        if test_case_id == 1:
            data = io.StringIO(
                """
            rs    alias  chrome  poston
            TP3      A/C      0    3
            TP7      A/T      0    7
            TP12     T/A      0   12
            TP15     C/A      0   15
            TP18     C/T      0   18
            """
            )
            df = pd.read_csv(data, delim_whitespace=True).set_index("rs")
            test = ["TP3", "TP18"]
        return df, test

    test_input = define_test_input(test_case_id)
    expected_result = generate_ans(copy.deepcopy(test_input))
    return test_input, expected_result

def exec_test(result, ans):
    try:
        pd.testing.assert_frame_equal(result, ans, check_dtype=False)
        return 1
    except:
        return 0

exec_context = r"""
import pandas as pd
import numpy as np
import io
df, test = test_input
[insert]
"""

def test_execution(solution: str):
    code = exec_context.replace("[insert]", solution)
    for i in range(1):
        test_input, expected_result = generate_test_case(i + 1)
        test_env = {"test_input": test_input}
        exec(code, test_env)
        assert exec_test(test_env["result"], expected_result)


Prompt:
Problem:
I am trying to delete rows from a Pandas dataframe using a list of row names, but it can't be done. Here is an example

# df
    alleles  chrom  pos strand  assembly#  center  protLSID  assayLSID
rs#
TP3      A/C      0    3      +        NaN     NaN       NaN        NaN
TP7      A/T      0    7      +        NaN     NaN       NaN        NaN
TP12     T/A      0   12      +        NaN     NaN       NaN        NaN
TP15     C/A      0   15      +        NaN     NaN       NaN        NaN
TP18     C/T      0   18      +        NaN     NaN       NaN        NaN

test = ['TP3','TP12','TP18']
Any help would be appreciated.

A:
<code>
import pandas as pd
import io

data = io.StringIO("""
rs  alleles  chrom  pos strand  assembly#  center  protLSID  assayLSID
TP3      A/C      0    3      +        NaN     NaN       NaN        NaN
TP7      A/T      0    7      +        NaN     NaN       NaN        NaN
TP12     T/A      0   12      +        NaN     NaN       NaN        NaN
TP15     C/A      0   15      +        NaN     NaN       NaN        NaN
TP18     C/T      0   18      +        NaN     NaN       NaN        NaN
""")
df = pd.read_csv(data, delim_whitespace=True).set_index('rs')
test = ['TP3', 'TP7', 'TP18']
</code>
result = ... # put solution in this variable
BEGIN SOLUTION
<code>

import pandas as pd
import numpy as np
import io
import copy

def generate_test_case(test_case_id):
    def generate_ans(data):
        data = data
        df, test = data
        return df.drop(test, inplace=False)

    def define_test_input(test_case_id):
        if test_case_id == 1:
            data = io.StringIO(
                """
            rs  alleles  chrom  pos strand  assembly#  center  protLSID  assayLSID
            TP3      A/C      0    3      +        NaN     NaN       NaN        NaN
            TP7      A/T      0    7      +        NaN     NaN       NaN        NaN
            TP12     T/A      0   12      +        NaN     NaN       NaN        NaN
            TP15     C/A      0   15      +        NaN     NaN       NaN        NaN
            TP18     C/T      0   18      +        NaN     NaN       NaN        NaN
            """
            )
            df = pd.read_csv(data, delim_whitespace=True).set_index("rs")
            test = ["TP3", "TP7", "TP18"]
        return df, test

    test_input = define_test_input(test_case_id)
    expected_result = generate_ans(copy.deepcopy(test_input))
    return test_input, expected_result

def exec_test(result, ans):
    try:
        pd.testing.assert_frame_equal(result, ans, check_dtype=False)
        return 1
    except:
        return 0

exec_context = r"""
import pandas as pd
import numpy as np
import io
df, test = test_input
[insert]
"""

def test_execution(solution: str):
    code = exec_context.replace("[insert]", solution)
    for i in range(1):
        test_input, expected_result = generate_test_case(i + 1)
        test_env = {"test_input": test_input}
        exec(code, test_env)
        assert exec_test(test_env["result"], expected_result)


Prompt:
Problem:
I am trying to extract rows from a Pandas dataframe using a list of row names according to the order of the list, but it can't be done. Note that the list might contain duplicate row names, and I just want the row occurs once. Here is an example

# df
    alleles  chrom  pos strand  assembly#  center  protLSID  assayLSID
rs#
TP3      A/C      0    3      +        NaN     NaN       NaN        NaN
TP7      A/T      0    7      +        NaN     NaN       NaN        NaN
TP12     T/A      0   12      +        NaN     NaN       NaN        NaN
TP15     C/A      0   15      +        NaN     NaN       NaN        NaN
TP18     C/T      0   18      +        NaN     NaN       NaN        NaN

test = ['TP3','TP12','TP18', 'TP3']

df.select(test)
This is what I was trying to do with just element of the list and I am getting this error TypeError: 'Index' object is not callable. What am I doing wrong?

A:
<code>
import pandas as pd

def f(df, test):
    # return the solution in this function
    # result = f(df, test)
    ### BEGIN SOLUTION

    result = df.loc[df.index.isin(test)]

    return result

import pandas as pd
import numpy as np
import io
import copy

def generate_test_case(test_case_id):
    def generate_ans(data):
        data = data
        df, test = data
        return df.loc[df.index.isin(test)]

    def define_test_input(test_case_id):
        if test_case_id == 1:
            data = io.StringIO(
                """
            rs  alleles  chrom  pos strand  assembly#  center  protLSID  assayLSID
            TP3      A/C      0    3      +        NaN     NaN       NaN        NaN
            TP7      A/T      0    7      +        NaN     NaN       NaN        NaN
            TP12     T/A      0   12      +        NaN     NaN       NaN        NaN
            TP15     C/A      0   15      +        NaN     NaN       NaN        NaN
            TP18     C/T      0   18      +        NaN     NaN       NaN        NaN
            """
            )
            df = pd.read_csv(data, delim_whitespace=True).set_index("rs")
            test = ["TP3", "TP7", "TP18", "TP3"]
        return df, test

    test_input = define_test_input(test_case_id)
    expected_result = generate_ans(copy.deepcopy(test_input))
    return test_input, expected_result

def exec_test(result, ans):
    try:
        pd.testing.assert_frame_equal(result, ans, check_dtype=False)
        return 1
    except:
        return 0

exec_context = r"""
import pandas as pd
import numpy as np
import io
df, test = test_input
def f(df, test):
[insert]
result = f(df,test)
"""

def test_execution(solution: str):
    code = exec_context.replace("[insert]", solution)
    for i in range(1):
        test_input, expected_result = generate_test_case(i + 1)
        test_env = {"test_input": test_input}
        exec(code, test_env)
        assert exec_test(test_env["result"], expected_result)


Prompt:
Problem:
I have a set of objects and their positions over time. I would like to get the distance between each car and their nearest neighbour, and calculate an average of this for each time point. An example dataframe is as follows:
 time = [0, 0, 0, 1, 1, 2, 2]
 x = [216, 218, 217, 280, 290, 130, 132]
 y = [13, 12, 12, 110, 109, 3, 56]
 car = [1, 2, 3, 1, 3, 4, 5]
 df = pd.DataFrame({'time': time, 'x': x, 'y': y, 'car': car})
 df
         x       y      car
 time
  0     216     13       1
  0     218     12       2
  0     217     12       3
  1     280     110      1
  1     290     109      3
  2     130     3        4
  2     132     56       5

For each time point, I would like to know the nearest car neighbour for each car. Example:
df2
          car    nearest_neighbour    euclidean_distance
 time
  0       1            3                    1.41
  0       2            3                    1.00
  0       3            2                    1.00
  1       1            3                    10.05
  1       3            1                    10.05
  2       4            5                    53.04
  2       5            4                    53.04

I know I can calculate the pairwise distances between cars from How to apply euclidean distance function to a groupby object in pandas dataframe? but how do I get the nearest neighbour for each car?
After that it seems simple enough to get an average of the distances for each frame using groupby, but it's the second step that really throws me off.
Help appreciated!

A:
<code>
import pandas as pd

time = [0, 0, 0, 1, 1, 2, 2]
x = [216, 218, 217, 280, 290, 130, 132]
y = [13, 12, 12, 110, 109, 3, 56]
car = [1, 2, 3, 1, 3, 4, 5]
df = pd.DataFrame({'time': time, 'x': x, 'y': y, 'car': car})
</code>
df = ... # put solution in this variable
BEGIN SOLUTION
<code>

import numpy as np
def g(df):
    time = df.time.tolist()
    car = df.car.tolist()
    nearest_neighbour = []
    euclidean_distance = []
    for i in range(len(df)):
        n = 0
        d = np.inf
        for j in range(len(df)):
            if df.loc[i, 'time'] == df.loc[j, 'time'] and df.loc[i, 'car'] != df.loc[j, 'car']:
                t = np.sqrt(((df.loc[i, 'x'] - df.loc[j, 'x'])**2) + ((df.loc[i, 'y'] - df.loc[j, 'y'])**2))
                if t < d:
                    d = t
                    n = df.loc[j, 'car']
        nearest_neighbour.append(n)
        euclidean_distance.append(d)
    return pd.DataFrame({'time': time, 'car': car, 'nearest_neighbour': nearest_neighbour, 'euclidean_distance': euclidean_distance})

df = g(df.copy())

import pandas as pd
import numpy as np
import copy

def generate_test_case(test_case_id):
    def generate_ans(data):
        df = data
        time = df.time.tolist()
        car = df.car.tolist()
        nearest_neighbour = []
        euclidean_distance = []
        for i in range(len(df)):
            n = 0
            d = np.inf
            for j in range(len(df)):
                if (
                    df.loc[i, "time"] == df.loc[j, "time"]
                    and df.loc[i, "car"] != df.loc[j, "car"]
                ):
                    t = np.sqrt(
                        ((df.loc[i, "x"] - df.loc[j, "x"]) ** 2)
                        + ((df.loc[i, "y"] - df.loc[j, "y"]) ** 2)
                    )
                    if t < d:
                        d = t
                        n = df.loc[j, "car"]
            nearest_neighbour.append(n)
            euclidean_distance.append(d)
        return pd.DataFrame(
            {
                "time": time,
                "car": car,
                "nearest_neighbour": nearest_neighbour,
                "euclidean_distance": euclidean_distance,
            }
        )

    def define_test_input(test_case_id):
        if test_case_id == 1:
            time = [0, 0, 0, 1, 1, 2, 2]
            x = [216, 218, 217, 280, 290, 130, 132]
            y = [13, 12, 12, 110, 109, 3, 56]
            car = [1, 2, 3, 1, 3, 4, 5]
            df = pd.DataFrame({"time": time, "x": x, "y": y, "car": car})
        if test_case_id == 2:
            time = [0, 0, 0, 1, 1, 2, 2]
            x = [219, 219, 216, 280, 290, 130, 132]
            y = [15, 11, 14, 110, 109, 3, 56]
            car = [1, 2, 3, 1, 3, 4, 5]
            df = pd.DataFrame({"time": time, "x": x, "y": y, "car": car})
        return df

    test_input = define_test_input(test_case_id)
    expected_result = generate_ans(copy.deepcopy(test_input))
    return test_input, expected_result

def exec_test(result, ans):
    try:
        result.euclidean_distance = np.round(result.euclidean_distance, 2)
        ans.euclidean_distance = np.round(ans.euclidean_distance, 2)
        pd.testing.assert_frame_equal(result, ans, check_dtype=False)
        return 1
    except:
        return 0

exec_context = r"""
import pandas as pd
import numpy as np
df = test_input
[insert]
result = df
"""

def test_execution(solution: str):
    code = exec_context.replace("[insert]", solution)
    for i in range(2):
        test_input, expected_result = generate_test_case(i + 1)
        test_env = {"test_input": test_input}
        exec(code, test_env)
        assert exec_test(test_env["result"], expected_result)


Prompt:
Problem:
I have a set of objects and their positions over time. I would like to get the distance between each car and their farmost neighbour, and calculate an average of this for each time point. An example dataframe is as follows:
 time = [0, 0, 0, 1, 1, 2, 2]
 x = [216, 218, 217, 280, 290, 130, 132]
 y = [13, 12, 12, 110, 109, 3, 56]
 car = [1, 2, 3, 1, 3, 4, 5]
 df = pd.DataFrame({'time': time, 'x': x, 'y': y, 'car': car})
 df
         x       y      car
 time
  0     216     13       1
  0     218     12       2
  0     217     12       3
  1     280     110      1
  1     290     109      3
  2     130     3        4
  2     132     56       5

For each time point, I would like to know the farmost car neighbour for each car. Example:
df2
   time  car   farmost_neighbour  euclidean_distance
0     0    1                  2            2.236068
1     0    2                  1            2.236068
2     0    3                  1            1.414214
3     1    1                  3           10.049876
4     1    3                  1           10.049876
5     2    4                  5           53.037722
6     2    5                  4           53.037722

I know I can calculate the pairwise distances between cars from How to apply euclidean distance function to a groupby object in pandas dataframe? but how do I get the farmost neighbour for each car?
After that it seems simple enough to get an average of the distances for each frame using groupby, but it's the second step that really throws me off.
Help appreciated!

A:
<code>
import pandas as pd

time = [0, 0, 0, 1, 1, 2, 2]
x = [216, 218, 217, 280, 290, 130, 132]
y = [13, 12, 12, 110, 109, 3, 56]
car = [1, 2, 3, 1, 3, 4, 5]
df = pd.DataFrame({'time': time, 'x': x, 'y': y, 'car': car})
</code>
df = ... # put solution in this variable
BEGIN SOLUTION
<code>

import numpy as np
def g(df):
    time = df.time.tolist()
    car = df.car.tolist()
    farmost_neighbour = []
    euclidean_distance = []
    for i in range(len(df)):
        n = 0
        d = 0
        for j in range(len(df)):
            if df.loc[i, 'time'] == df.loc[j, 'time'] and df.loc[i, 'car'] != df.loc[j, 'car']:
                t = np.sqrt(((df.loc[i, 'x'] - df.loc[j, 'x'])**2) + ((df.loc[i, 'y'] - df.loc[j, 'y'])**2))
                if t >= d:
                    d = t
                    n = df.loc[j, 'car']
        farmost_neighbour.append(n)
        euclidean_distance.append(d)
    return pd.DataFrame({'time': time, 'car': car, 'farmost_neighbour': farmost_neighbour, 'euclidean_distance': euclidean_distance})

df = g(df.copy())

import pandas as pd
import numpy as np
import copy

def generate_test_case(test_case_id):
    def generate_ans(data):
        df = data
        time = df.time.tolist()
        car = df.car.tolist()
        farmost_neighbour = []
        euclidean_distance = []
        for i in range(len(df)):
            n = 0
            d = 0
            for j in range(len(df)):
                if (
                    df.loc[i, "time"] == df.loc[j, "time"]
                    and df.loc[i, "car"] != df.loc[j, "car"]
                ):
                    t = np.sqrt(
                        ((df.loc[i, "x"] - df.loc[j, "x"]) ** 2)
                        + ((df.loc[i, "y"] - df.loc[j, "y"]) ** 2)
                    )
                    if t >= d:
                        d = t
                        n = df.loc[j, "car"]
            farmost_neighbour.append(n)
            euclidean_distance.append(d)
        return pd.DataFrame(
            {
                "time": time,
                "car": car,
                "farmost_neighbour": farmost_neighbour,
                "euclidean_distance": euclidean_distance,
            }
        )

    def define_test_input(test_case_id):
        if test_case_id == 1:
            time = [0, 0, 0, 1, 1, 2, 2]
            x = [216, 218, 217, 280, 290, 130, 132]
            y = [13, 12, 12, 110, 109, 3, 56]
            car = [1, 2, 3, 1, 3, 4, 5]
            df = pd.DataFrame({"time": time, "x": x, "y": y, "car": car})
        if test_case_id == 2:
            time = [0, 0, 0, 1, 1, 2, 2]
            x = [219, 219, 216, 280, 290, 130, 132]
            y = [15, 11, 14, 110, 109, 3, 56]
            car = [1, 2, 3, 1, 3, 4, 5]
            df = pd.DataFrame({"time": time, "x": x, "y": y, "car": car})
        return df

    test_input = define_test_input(test_case_id)
    expected_result = generate_ans(copy.deepcopy(test_input))
    return test_input, expected_result

def exec_test(result, ans):
    try:
        result.euclidean_distance = np.round(result.euclidean_distance, 2)
        ans.euclidean_distance = np.round(ans.euclidean_distance, 2)
        pd.testing.assert_frame_equal(result, ans, check_dtype=False)
        return 1
    except:
        return 0

exec_context = r"""
import pandas as pd
import numpy as np
df = test_input
[insert]
result = df
"""

def test_execution(solution: str):
    code = exec_context.replace("[insert]", solution)
    for i in range(2):
        test_input, expected_result = generate_test_case(i + 1)
        test_env = {"test_input": test_input}
        exec(code, test_env)
        assert exec_test(test_env["result"], expected_result)


Prompt:
Problem:
My sample df has four columns with NaN values. The goal is to concatenate all the rows while excluding the NaN values.
import pandas as pd
import numpy as np
df = pd.DataFrame({'keywords_0':["a", np.nan, "c"],
                'keywords_1':["d", "e", np.nan],
                'keywords_2':[np.nan, np.nan, "b"],
                'keywords_3':["f", np.nan, "g"]})
  keywords_0 keywords_1 keywords_2 keywords_3
0          a          d        NaN          f
1        NaN          e        NaN        NaN
2          c        NaN          b          g

Want to accomplish the following:
  keywords_0 keywords_1 keywords_2 keywords_3 keywords_all
0          a          d        NaN          f        a,d,f
1        NaN          e        NaN        NaN            e
2          c        NaN          b          g        c,b,g

Pseudo code:
cols = [df.keywords_0, df.keywords_1, df.keywords_2, df.keywords_3]
df["keywords_all"] = df["keywords_all"].apply(lambda cols: ",".join(cols), axis=1)

I know I can use ",".join() to get the exact result, but I am unsure how to pass the column names into the function.

A:
<code>
import pandas as pd
import numpy as np

df = pd.DataFrame({'keywords_0':["a", np.nan, "c"],
                'keywords_1':["d", "e", np.nan],
                'keywords_2':[np.nan, np.nan, "b"],
                'keywords_3':["f", np.nan, "g"]})
</code>
df = ... # put solution in this variable
BEGIN SOLUTION
<code>

import numpy as np
def g(df):
    df["keywords_all"] = df.apply(lambda x: ','.join(x.dropna()), axis=1)
    return df

df = g(df.copy())

import pandas as pd
import numpy as np
import copy

def generate_test_case(test_case_id):
    def generate_ans(data):
        df = data
        df["keywords_all"] = df.apply(lambda x: ",".join(x.dropna()), axis=1)
        return df

    def define_test_input(test_case_id):
        if test_case_id == 1:
            df = pd.DataFrame(
                {
                    "keywords_0": ["a", np.nan, "c"],
                    "keywords_1": ["d", "e", np.nan],
                    "keywords_2": [np.nan, np.nan, "b"],
                    "keywords_3": ["f", np.nan, "g"],
                }
            )
        if test_case_id == 2:
            df = pd.DataFrame(
                {
                    "keywords_0": ["a", np.nan, "c"],
                    "keywords_1": ["b", "g", np.nan],
                    "keywords_2": [np.nan, np.nan, "j"],
                    "keywords_3": ["d", np.nan, "b"],
                }
            )
        return df

    test_input = define_test_input(test_case_id)
    expected_result = generate_ans(copy.deepcopy(test_input))
    return test_input, expected_result

def exec_test(result, ans):
    try:
        pd.testing.assert_frame_equal(result, ans, check_dtype=False)
        return 1
    except:
        return 0

exec_context = r"""
import pandas as pd
import numpy as np
df = test_input
[insert]
result = df
"""

def test_execution(solution: str):
    code = exec_context.replace("[insert]", solution)
    for i in range(2):
        test_input, expected_result = generate_test_case(i + 1)
        test_env = {"test_input": test_input}
        exec(code, test_env)
        assert exec_test(test_env["result"], expected_result)


Prompt:
Problem:
My sample df has four columns with NaN values. The goal is to concatenate all the rows while excluding the NaN values.
import pandas as pd
import numpy as np
df = pd.DataFrame({'keywords_0':["a", np.nan, "c"],
                'keywords_1':["d", "e", np.nan],
                'keywords_2':[np.nan, np.nan, "b"],
                'keywords_3':["f", np.nan, "g"]})
  keywords_0 keywords_1 keywords_2 keywords_3
0          a          d        NaN          f
1        NaN          e        NaN        NaN
2          c        NaN          b          g

Want to accomplish the following:
  keywords_0 keywords_1 keywords_2 keywords_3 keywords_all
0          a          d        NaN          f        a-d-f
1        NaN          e        NaN        NaN            e
2          c        NaN          b          g        c-b-g

Pseudo code:
cols = [df.keywords_0, df.keywords_1, df.keywords_2, df.keywords_3]
df["keywords_all"] = df["keywords_all"].apply(lambda cols: "-".join(cols), axis=1)

I know I can use "-".join() to get the exact result, but I am unsure how to pass the column names into the function.

A:
<code>
import pandas as pd
import numpy as np

df = pd.DataFrame({'keywords_0':["a", np.nan, "c"],
                'keywords_1':["d", "e", np.nan],
                'keywords_2':[np.nan, np.nan, "b"],
                'keywords_3':["f", np.nan, "g"]})
</code>
df = ... # put solution in this variable
BEGIN SOLUTION
<code>

import numpy as np
def g(df):
    df["keywords_all"] = df.apply(lambda x: '-'.join(x.dropna()), axis=1)
    return df

df = g(df.copy())

import pandas as pd
import numpy as np
import copy

def generate_test_case(test_case_id):
    def generate_ans(data):
        df = data
        df["keywords_all"] = df.apply(lambda x: "-".join(x.dropna()), axis=1)
        return df

    def define_test_input(test_case_id):
        if test_case_id == 1:
            df = pd.DataFrame(
                {
                    "keywords_0": ["a", np.nan, "c"],
                    "keywords_1": ["d", "e", np.nan],
                    "keywords_2": [np.nan, np.nan, "b"],
                    "keywords_3": ["f", np.nan, "g"],
                }
            )
        if test_case_id == 2:
            df = pd.DataFrame(
                {
                    "keywords_0": ["a", np.nan, "c"],
                    "keywords_1": ["b", "g", np.nan],
                    "keywords_2": [np.nan, np.nan, "j"],
                    "keywords_3": ["d", np.nan, "b"],
                }
            )
        return df

    test_input = define_test_input(test_case_id)
    expected_result = generate_ans(copy.deepcopy(test_input))
    return test_input, expected_result

def exec_test(result, ans):
    try:
        pd.testing.assert_frame_equal(result, ans, check_dtype=False)
        return 1
    except:
        return 0

exec_context = r"""
import pandas as pd
import numpy as np
df = test_input
[insert]
result = df
"""

def test_execution(solution: str):
    code = exec_context.replace("[insert]", solution)
    for i in range(2):
        test_input, expected_result = generate_test_case(i + 1)
        test_env = {"test_input": test_input}
        exec(code, test_env)
        assert exec_test(test_env["result"], expected_result)


Prompt:
Problem:
My sample df has four columns with NaN values. The goal is to concatenate all the keywords rows while excluding the NaN values.
import pandas as pd
import numpy as np
df = pd.DataFrame({'users': ['Hu Tao', 'Zhongli', 'Xingqiu'],
                   'keywords_0': ["a", np.nan, "c"],
                   'keywords_1': ["d", "e", np.nan],
                   'keywords_2': [np.nan, np.nan, "b"],
                   'keywords_3': ["f", np.nan, "g"]})

     users keywords_0 keywords_1 keywords_2 keywords_3
0   Hu Tao          a          d        NaN          f
1  Zhongli        NaN          e        NaN        NaN
2  Xingqiu          c        NaN          b          g

Want to accomplish the following:
     users keywords_0 keywords_1 keywords_2 keywords_3 keywords_all
0   Hu Tao          a          d        NaN          f        a-d-f
1  Zhongli        NaN          e        NaN        NaN            e
2  Xingqiu          c        NaN          b          g        c-b-g

Pseudo code:
cols = [df.keywords_0, df.keywords_1, df.keywords_2, df.keywords_3]
df["keywords_all"] = df["keywords_all"].apply(lambda cols: "-".join(cols), axis=1)

I know I can use "-".join() to get the exact result, but I am unsure how to pass the column names into the function.

A:
<code>
import pandas as pd
import numpy as np

df = pd.DataFrame({'users': ['Hu Tao', 'Zhongli', 'Xingqiu'],
                   'keywords_0': ["a", np.nan, "c"],
                   'keywords_1': ["d", "e", np.nan],
                   'keywords_2': [np.nan, np.nan, "b"],
                   'keywords_3': ["f", np.nan, "g"]})
</code>
df = ... # put solution in this variable
BEGIN SOLUTION
<code>

import numpy as np
def g(df):
    df["keywords_all"] = df.filter(like='keyword').apply(lambda x: '-'.join(x.dropna()), axis=1)
    return df

df = g(df.copy())

import pandas as pd
import numpy as np
import copy

def generate_test_case(test_case_id):
    def generate_ans(data):
        df = data
        df["keywords_all"] = df.filter(like="keyword").apply(
            lambda x: "-".join(x.dropna()), axis=1
        )
        return df

    def define_test_input(test_case_id):
        if test_case_id == 1:
            df = pd.DataFrame(
                {
                    "users": ["Hu Tao", "Zhongli", "Xingqiu"],
                    "keywords_0": ["a", np.nan, "c"],
                    "keywords_1": ["d", "e", np.nan],
                    "keywords_2": [np.nan, np.nan, "b"],
                    "keywords_3": ["f", np.nan, "g"],
                }
            )
        if test_case_id == 2:
            df = pd.DataFrame(
                {
                    "users": ["Hu Tao", "Zhongli", "Xingqiu"],
                    "keywords_0": ["a", np.nan, "c"],
                    "keywords_1": ["b", "g", np.nan],
                    "keywords_2": [np.nan, np.nan, "j"],
                    "keywords_3": ["d", np.nan, "b"],
                }
            )
        return df

    test_input = define_test_input(test_case_id)
    expected_result = generate_ans(copy.deepcopy(test_input))
    return test_input, expected_result

def exec_test(result, ans):
    try:
        pd.testing.assert_frame_equal(result, ans, check_dtype=False)
        return 1
    except:
        return 0

exec_context = r"""
import pandas as pd
import numpy as np
df = test_input
[insert]
result = df
"""

def test_execution(solution: str):
    code = exec_context.replace("[insert]", solution)
    for i in range(2):
        test_input, expected_result = generate_test_case(i + 1)
        test_env = {"test_input": test_input}
        exec(code, test_env)
        assert exec_test(test_env["result"], expected_result)


Prompt:
Problem:
My sample df has four columns with NaN values. The goal is to concatenate all the kewwords rows from end to front while excluding the NaN values.
import pandas as pd
import numpy as np
df = pd.DataFrame({'users': ['Hu Tao', 'Zhongli', 'Xingqiu'],
                   'keywords_0': ["a", np.nan, "c"],
                   'keywords_1': ["d", "e", np.nan],
                   'keywords_2': [np.nan, np.nan, "b"],
                   'keywords_3': ["f", np.nan, "g"]})

     users keywords_0 keywords_1 keywords_2 keywords_3
0   Hu Tao          a          d        NaN          f
1  Zhongli        NaN          e        NaN        NaN
2  Xingqiu          c        NaN          b          g

Want to accomplish the following:
     users keywords_0 keywords_1 keywords_2 keywords_3 keywords_all
0   Hu Tao          a          d        NaN          f        f-d-a
1  Zhongli        NaN          e        NaN        NaN            e
2  Xingqiu          c        NaN          b          g        g-b-c

Pseudo code:
cols = [df.keywords_0, df.keywords_1, df.keywords_2, df.keywords_3]
df["keywords_all"] = df["keywords_all"].apply(lambda cols: "-".join(cols), axis=1)

I know I can use "-".join() to get the exact result, but I am unsure how to pass the column names into the function.

A:
<code>
import pandas as pd
import numpy as np

df = pd.DataFrame({'users': ['Hu Tao', 'Zhongli', 'Xingqiu'],
                   'keywords_0': ["a", np.nan, "c"],
                   'keywords_1': ["d", "e", np.nan],
                   'keywords_2': [np.nan, np.nan, "b"],
                   'keywords_3': ["f", np.nan, "g"]})
</code>
df = ... # put solution in this variable
BEGIN SOLUTION
<code>

import numpy as np
def g(df):
    df["keywords_all"] = df.filter(like='keyword').apply(lambda x: '-'.join(x.dropna()), axis=1)
    for i in range(len(df)):
        df.loc[i, "keywords_all"] = df.loc[i, "keywords_all"][::-1]
    return df

df = g(df.copy())

import pandas as pd
import numpy as np
import copy

def generate_test_case(test_case_id):
    def generate_ans(data):
        df = data
        df["keywords_all"] = df.filter(like="keyword").apply(
            lambda x: "-".join(x.dropna()), axis=1
        )
        for i in range(len(df)):
            df.loc[i, "keywords_all"] = df.loc[i, "keywords_all"][::-1]
        return df

    def define_test_input(test_case_id):
        if test_case_id == 1:
            df = pd.DataFrame(
                {
                    "users": ["Hu Tao", "Zhongli", "Xingqiu"],
                    "keywords_0": ["a", np.nan, "c"],
                    "keywords_1": ["d", "e", np.nan],
                    "keywords_2": [np.nan, np.nan, "b"],
                    "keywords_3": ["f", np.nan, "g"],
                }
            )
        if test_case_id == 2:
            df = pd.DataFrame(
                {
                    "users": ["Hu Tao", "Zhongli", "Xingqiu"],
                    "keywords_0": ["a", np.nan, "c"],
                    "keywords_1": ["b", "g", np.nan],
                    "keywords_2": [np.nan, np.nan, "j"],
                    "keywords_3": ["d", np.nan, "b"],
                }
            )
        return df

    test_input = define_test_input(test_case_id)
    expected_result = generate_ans(copy.deepcopy(test_input))
    return test_input, expected_result

def exec_test(result, ans):
    try:
        pd.testing.assert_frame_equal(result, ans, check_dtype=False)
        return 1
    except:
        return 0

exec_context = r"""
import pandas as pd
import numpy as np
df = test_input
[insert]
result = df
"""

def test_execution(solution: str):
    code = exec_context.replace("[insert]", solution)
    for i in range(2):
        test_input, expected_result = generate_test_case(i + 1)
        test_env = {"test_input": test_input}
        exec(code, test_env)
        assert exec_test(test_env["result"], expected_result)


Prompt:
Problem:
I have a pandas Dataframe like below:
UserId    ProductId    Quantity
1         1            6
1         4            1
1         7            3
2         4            2
3         2            7
3         1            2

Now, I want to randomly select the 20% of rows of this DataFrame, using df.sample(n), set random_state=0 and change the value of the Quantity column of these rows to zero. I would also like to keep the indexes of the altered rows. So the resulting DataFrame would be:
UserId    ProductId    Quantity
1         1            6
1         4            1
1         7            3
2         4            0
3         2            7
3         1            0

A:
<code>
import pandas as pd

df = pd.DataFrame({'UserId': [1, 1, 1, 2, 3, 3],
                   'ProductId': [1, 4, 7, 4, 2, 1],
                   'Quantity': [6, 1, 3, 2, 7, 2]})
</code>
df = ... # put solution in this variable
BEGIN SOLUTION
<code>

def g(df):
    l = int(0.2 * len(df))
    dfupdate = df.sample(l, random_state=0)
    dfupdate.Quantity = 0
    df.update(dfupdate)
    return df

df = g(df.copy())

import pandas as pd
import numpy as np
import copy
import tokenize, io

def generate_test_case(test_case_id):
    def generate_ans(data):
        df = data
        l = int(0.2 * len(df))
        dfupdate = df.sample(l, random_state=0)
        dfupdate.Quantity = 0
        df.update(dfupdate)
        return df

    def define_test_input(test_case_id):
        if test_case_id == 1:
            df = pd.DataFrame(
                {
                    "UserId": [1, 1, 1, 2, 3, 3],
                    "ProductId": [1, 4, 7, 4, 2, 1],
                    "Quantity": [6, 1, 3, 2, 7, 2],
                }
            )
        if test_case_id == 2:
            df = pd.DataFrame(
                {
                    "UserId": [1, 1, 1, 2, 3, 3, 1, 1, 4],
                    "ProductId": [1, 4, 7, 4, 2, 1, 5, 1, 4],
                    "Quantity": [6, 1, 3, 2, 7, 2, 9, 9, 6],
                }
            )
        return df

    test_input = define_test_input(test_case_id)
    expected_result = generate_ans(copy.deepcopy(test_input))
    return test_input, expected_result

def exec_test(result, ans):
    try:
        pd.testing.assert_frame_equal(result, ans, check_dtype=False)
        return 1
    except:
        return 0

exec_context = r"""
import pandas as pd
import numpy as np
df = test_input
[insert]
result = df
"""

def test_execution(solution: str):
    code = exec_context.replace("[insert]", solution)
    for i in range(2):
        test_input, expected_result = generate_test_case(i + 1)
        test_env = {"test_input": test_input}
        exec(code, test_env)
        assert exec_test(test_env["result"], expected_result)

def test_string(solution: str):
    tokens = []
    for token in tokenize.tokenize(io.BytesIO(solution.encode("utf-8")).readline):
        tokens.append(token.string)
    assert "sample" in tokens


Prompt:
Problem:
I have a pandas Dataframe like below:
UserId    ProductId    Quantity
1         1            6
1         4            1
1         7            3
2         4            2
3         2            7
3         1            2

Now, I want to randomly select the 20% of rows of this DataFrame, using df.sample(n), set random_state=0 and change the value of the ProductId column of these rows to zero. I would also like to keep the indexes of the altered rows. So the resulting DataFrame would be:
UserId    ProductId    Quantity
1         1            6
1         4            1
1         7            3
2         0            2
3         2            7
3         0            2

A:
<code>
import pandas as pd

df = pd.DataFrame({'UserId': [1, 1, 1, 2, 3, 3],
                   'ProductId': [1, 4, 7, 4, 2, 1],
                   'Quantity': [6, 1, 3, 2, 7, 2]})
</code>
df = ... # put solution in this variable
BEGIN SOLUTION
<code>

def g(df):
    l = int(0.2 * len(df))
    dfupdate = df.sample(l, random_state=0)
    dfupdate.ProductId = 0
    df.update(dfupdate)
    return df

df = g(df.copy())

import pandas as pd
import numpy as np
import copy
import tokenize, io

def generate_test_case(test_case_id):
    def generate_ans(data):
        df = data
        l = int(0.2 * len(df))
        dfupdate = df.sample(l, random_state=0)
        dfupdate.ProductId = 0
        df.update(dfupdate)
        return df

    def define_test_input(test_case_id):
        if test_case_id == 1:
            df = pd.DataFrame(
                {
                    "UserId": [1, 1, 1, 2, 3, 3],
                    "ProductId": [1, 4, 7, 4, 2, 1],
                    "Quantity": [6, 1, 3, 2, 7, 2],
                }
            )
        if test_case_id == 2:
            df = pd.DataFrame(
                {
                    "UserId": [1, 1, 1, 2, 3, 3, 1, 1, 4],
                    "ProductId": [1, 4, 7, 4, 2, 1, 5, 1, 4],
                    "Quantity": [6, 1, 3, 2, 7, 2, 9, 9, 6],
                }
            )
        return df

    test_input = define_test_input(test_case_id)
    expected_result = generate_ans(copy.deepcopy(test_input))
    return test_input, expected_result

def exec_test(result, ans):
    try:
        pd.testing.assert_frame_equal(result, ans, check_dtype=False)
        return 1
    except:
        return 0

exec_context = r"""
import pandas as pd
import numpy as np
df = test_input
[insert]
result = df
"""

def test_execution(solution: str):
    code = exec_context.replace("[insert]", solution)
    for i in range(2):
        test_input, expected_result = generate_test_case(i + 1)
        test_env = {"test_input": test_input}
        exec(code, test_env)
        assert exec_test(test_env["result"], expected_result)

def test_string(solution: str):
    tokens = []
    for token in tokenize.tokenize(io.BytesIO(solution.encode("utf-8")).readline):
        tokens.append(token.string)
    assert "sample" in tokens


Prompt:
Problem:
I have a pandas Dataframe like below:
    UserId  ProductId  Quantity
0        1          1         6
1        1          4         1
2        1          7         3
3        1          4         2
4        1          2         7
5        2          1         2
6        2          1         6
7        2          4         1
8        2          7         3
9        2          4         2
10       3          2         7
11       3          1         2
12       3          1         6
13       3          4         1
14       3          7         3

Now, I want to randomly select the 20% of rows of each user, using df.sample(n), set random_state=0 and change the value of the Quantity column of these rows to zero. I would also like to keep the indexes of the altered rows. So the resulting DataFrame would be:
    UserId  ProductId  Quantity
0      1.0        1.0       6.0
1      1.0        4.0       1.0
2      1.0        7.0       0.0
3      1.0        4.0       2.0
4      1.0        2.0       7.0
5      2.0        1.0       2.0
6      2.0        1.0       6.0
7      2.0        4.0       0.0
8      2.0        7.0       3.0
9      2.0        4.0       2.0
10     3.0        2.0       7.0
11     3.0        1.0       2.0
12     3.0        1.0       0.0
13     3.0        4.0       1.0
14     3.0        7.0       3.0

A:
<code>
import pandas as pd

df = pd.DataFrame({'UserId': [1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 3, 3, 3, 3, 3],
                   'ProductId': [1, 4, 7, 4, 2, 1, 1, 4, 7, 4, 2, 1, 1, 4, 7],
                   'Quantity': [6, 1, 3, 2, 7, 2, 6, 1, 3, 2, 7, 2, 6, 1, 3]})
</code>
df = ... # put solution in this variable
BEGIN SOLUTION
<code>

def g(df):
    for i in range(len(df)):
        tot = 0
        if i != 0:
            if df.loc[i, 'UserId'] == df.loc[i-1, 'UserId']:
                continue
        for j in range(len(df)):
            if df.loc[i, 'UserId'] == df.loc[j, 'UserId']:
                tot += 1
        l = int(0.2*tot)
        dfupdate = df.iloc[i:i+tot].sample(l, random_state=0)
        dfupdate.Quantity = 0
        df.update(dfupdate)
    return df

df = g(df.copy())

import pandas as pd
import numpy as np
import copy
import tokenize, io

def generate_test_case(test_case_id):
    def generate_ans(data):
        df = data
        for i in range(len(df)):
            tot = 0
            if i != 0:
                if df.loc[i, "UserId"] == df.loc[i - 1, "UserId"]:
                    continue
            for j in range(len(df)):
                if df.loc[i, "UserId"] == df.loc[j, "UserId"]:
                    tot += 1
            l = int(0.2 * tot)
            dfupdate = df.iloc[i : i + tot].sample(l, random_state=0)
            dfupdate.Quantity = 0
            df.update(dfupdate)
        return df

    def define_test_input(test_case_id):
        if test_case_id == 1:
            df = pd.DataFrame(
                {
                    "UserId": [1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 3, 3, 3, 3, 3],
                    "ProductId": [1, 4, 7, 4, 2, 1, 1, 4, 7, 4, 2, 1, 1, 4, 7],
                    "Quantity": [6, 1, 3, 2, 7, 2, 6, 1, 3, 2, 7, 2, 6, 1, 3],
                }
            )
        if test_case_id == 2:
            df = pd.DataFrame(
                {
                    "UserId": [1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 3, 3, 3, 3, 3],
                    "ProductId": [6, 1, 3, 2, 7, 2, 6, 1, 3, 2, 7, 2, 6, 1, 3],
                    "Quantity": [1, 4, 7, 4, 2, 1, 1, 4, 7, 4, 2, 1, 1, 4, 7],
                }
            )
        return df

    test_input = define_test_input(test_case_id)
    expected_result = generate_ans(copy.deepcopy(test_input))
    return test_input, expected_result

def exec_test(result, ans):
    try:
        pd.testing.assert_frame_equal(result, ans, check_dtype=False)
        return 1
    except:
        return 0

exec_context = r"""
import pandas as pd
import numpy as np
df = test_input
[insert]
result = df
"""

def test_execution(solution: str):
    code = exec_context.replace("[insert]", solution)
    for i in range(2):
        test_input, expected_result = generate_test_case(i + 1)
        test_env = {"test_input": test_input}
        exec(code, test_env)
        assert exec_test(test_env["result"], expected_result)

def test_string(solution: str):
    tokens = []
    for token in tokenize.tokenize(io.BytesIO(solution.encode("utf-8")).readline):
        tokens.append(token.string)
    assert "sample" in tokens


Prompt:
Problem:
I am trying to find duplicates rows in a pandas dataframe.
df=pd.DataFrame(data=[[1,2],[3,4],[1,2],[1,4],[1,2]],columns=['col1','col2'])
df
Out[15]:
   col1  col2
0     1     2
1     3     4
2     1     2
3     1     4
4     1     2
duplicate_bool = df.duplicated(subset=['col1','col2'], keep='first')
duplicate = df.loc[duplicate_bool == True]
duplicate
Out[16]:
   col1  col2
2     1     2
4     1     2

Is there a way to add a column referring to the index of the first duplicate (the one kept)
duplicate
Out[16]:
   col1  col2  index_original
2     1     2               0
4     1     2               0

Note: df could be very very big in my case....

A:
<code>
import pandas as pd

df=pd.DataFrame(data=[[1,2],[3,4],[1,2],[1,4],[1,2]],columns=['col1','col2'])
</code>
result = ... # put solution in this variable
BEGIN SOLUTION
<code>

def g(df):
    df['index_original'] = df.groupby(['col1', 'col2']).col1.transform('idxmin')
    return df[df.duplicated(subset=['col1', 'col2'], keep='first')]

result = g(df.copy())

import pandas as pd
import numpy as np
import copy

def generate_test_case(test_case_id):
    def generate_ans(data):
        df = data
        df["index_original"] = df.groupby(["col1", "col2"]).col1.transform("idxmin")
        return df[df.duplicated(subset=["col1", "col2"], keep="first")]

    def define_test_input(test_case_id):
        if test_case_id == 1:
            df = pd.DataFrame(
                data=[[1, 2], [3, 4], [1, 2], [1, 4], [1, 2]], columns=["col1", "col2"]
            )
        if test_case_id == 2:
            df = pd.DataFrame(
                data=[[1, 1], [3, 1], [1, 4], [1, 9], [1, 6]], columns=["col1", "col2"]
            )
        return df

    test_input = define_test_input(test_case_id)
    expected_result = generate_ans(copy.deepcopy(test_input))
    return test_input, expected_result

def exec_test(result, ans):
    try:
        pd.testing.assert_frame_equal(result, ans, check_dtype=False)
        return 1
    except:
        return 0

exec_context = r"""
import pandas as pd
import numpy as np
df = test_input
[insert]
"""

def test_execution(solution: str):
    code = exec_context.replace("[insert]", solution)
    for i in range(2):
        test_input, expected_result = generate_test_case(i + 1)
        test_env = {"test_input": test_input}
        exec(code, test_env)
        assert exec_test(test_env["result"], expected_result)