content stringlengths 0 3.01k | quality_feedback stringlengths 0 8 |
|---|---|
import matplotlib
matplotlib.use("Agg")
import matplotlib.pyplot as plt
import statsmodels.api as sm
import statsmodels.formula.api as smf
import numpy as np
import math
##AUTOMATICLY! LOVELY!!
########################
import pandas as pd #<--- pitty! 0.14.1 when 0.16 is already there!!
import sklearn as sk
def get_f... | 4 5 5 |
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import seaborn as sns
train = pd.read_csv("../input/train.csv")
print(train.head())
plt.figure()
sns.pairplot(data=train[["Age","Sex"]])
plt.savefig("1_pair.png")
| 5 8 9 |
import numpy as np
import pandas as pd
#Print you can execute arbitrary python code
train = pd.read_csv("../input/train.csv", dtype={"Age": np.float64}, )
test = pd.read_csv("../input/test.csv", dtype={"Age": np.float64}, )
#TRAIN DATA SET
#Print to standard output, and see the results in the "log" section below af... | 3 4 8 |
#This script looks at the survival rate for those with the most common names
import numpy as np
import pandas as pd
train = pd.read_csv("../input/train.csv", dtype={"Age": np.float64}, )
dfTitanic = train
#Extract the first name from passenger name
dfTitanic['FirstName'] = dfTitanic['Name'].str.extract('(Mr\. |Miss\... | 6 8 8 |
import numpy as np
import pandas as pd
train = pd.read_csv("../input/train.csv", dtype={"Age": np.float64}, )
dfTitanic = train
dfTitanic['Male'] = dfTitanic['Sex'] == 'male'
dfTitanic['Name'].head(10)
dfTitanic['FirstName'] = dfTitanic['Name'].str.extract('(Mr\. |Miss\. |Master. |Mrs\.[A-Za-z ]*\()([A-Za-z]*)')[1]
d... | 4 7 8 |
# Some fancy Python:
import datetime
from time import sleep
print(datetime.datetime.now())
sleep(1)
print(datetime.datetime.now()) | 10 10 10 |
import matplotlib
matplotlib.use("Agg")
import matplotlib.pyplot as plt
import statsmodels.api as sm
import statsmodels.formula.api as smf
import numpy as np
import math
##AUTOMATICLY! LOVELY!!
########################
import pandas as pd #<--- pitty! 0.14.1 when 0.16 is already there!!
import sklearn as sk
def get_f... | 5 7 5 |
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import seaborn as sns
train = pd.read_csv("../input/train.csv", parse_dates=["datetime"])
train["hour"] = pd.DatetimeIndex(train['datetime']).hour
train["month"] = pd.DatetimeIndex(train['datetime']).month
train["temp"] = train.temp*9.0/5.0+32.0
t... | 6 8 9 |
print("Rate Limit Test")
| 10 10 10 |
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import seaborn as sns
train = pd.read_csv("../input/train.csv", dtype={"Age": np.float64}, )
# Replacing missing ages with median
train["Age"][np.isnan(train["Age"])] = np.median(train["Age"])
train["Survived"][train["Survived"]==1] = "Survived"
t... | 5 8 9 |
"""
Simple demo of a horizontal bar chart.
"""
import matplotlib
matplotlib.use("Agg")
import matplotlib.pyplot as plt; plt.rcdefaults()
import numpy as np
import pandas as pd
train = pd.read_csv("../input/train.csv", parse_dates=["datetime"])
train["hour"] = pd.DatetimeIndex(train['datetime']).hour
train["temp"] = t... | 4 8 8 |
# This script shows you how to make a submission using a few
# useful Python libraries.
# It gets a public leaderboard score of 0.76077.
# Maybe you can tweak it and do better...?
import pandas as pd
import xgboost as xgb
from sklearn.preprocessing import LabelEncoder
import numpy as np
# Load the data
train_df = pd.... | 8 9 9 |
0 0 0 | |
print("Rate Limit Test")
| 10 10 10 |
import csv
from collections import defaultdict
from math import log10
import plt
#Get counts for starting digit in columns: counts, registered, casual
d = defaultdict(int)
for e, row in enumerate(csv.DictReader(open("../input/train.csv"))):
d[row["count"][0]] += 1
d[row["registered"][0]] += 1
d[row["casual"][0]] +=... | 3 7 7 |
print("Rate Limit Test")
| 10 10 10 |
import numpy as np
import pandas as pd
import csv as csv
#Print you can execute arbitrary python code
train = pd.read_csv("../input/train.csv", dtype={"Age": np.float64}, )
test = pd.read_csv("../input/test.csv", dtype={"Age": np.float64}, )
csv_file_object = csv.reader('.../input/train.csv', 'rb')
#TRAIN DATA SET... | 3 5 8 |
import matplotlib
matplotlib.use("Agg")
import matplotlib.pyplot as plt
import statsmodels.api as sm
import statsmodels.formula.api as smf
import numpy as np
import math
##AUTOMATICLY! LOVELY!!
########################
import pandas as pd #<--- pitty! 0.14.1 when 0.16 is already there!!
import sklearn as sk
def get_f... | |
#This script looks at the survival rate for those with the most common names
import numpy as np
import pandas as pd
train = pd.read_csv("../input/train.csv", dtype={"Age": np.float64}, )
dfTitanic = train
#Extract the first name from passenger name
dfTitanic['FirstName'] = dfTitanic['Name'].str.extract('(Mr\. |Miss\... | 7 8 9 |
import matplotlib
matplotlib.use("Agg")
import statsmodels.api as sm
import statsmodels.formula.api as smf
import numpy as np
##AUTOMATICLY! LOVELY!!
########################
import pandas as pd #<--- pitty! 0.14.1 when 0.16 is already there!!
import sklearn as sk
def get_feature_mat(fname):
#feature engineering in ... | 5 7 7 |
0 0 0 | |
import matplotlib
matplotlib.use("Agg")
import matplotlib.pyplot as plt; plt.rcdefaults()
import numpy as np
import pandas as pd
train = pd.read_csv("../input/train.csv")
train.head()
train.head().transpose()
train["TitleLength"] = train.Title.apply(len)
import matplotlib.pyplot as plt
import numpy as np
plt.hist([t... | 5 7 8 |
import sys
import numpy as np
from sklearn import cross_validation
import pandas as pd
import csv
from sklearn.grid_search import GridSearchCV
from sklearn.decomposition import RandomizedPCA
from sklearn.decomposition import SparsePCA
from sklearn.svm import SVC
def targetFeatureSplit( data ):
target = []
feat... | 6 7 8 |
import numpy as np
import pandas as pd
#Print you can execute arbitrary python code
train = pd.read_csv("../input/train.csv", dtype={"Age": np.float64}, )
test = pd.read_csv("../input/test.csv", dtype={"Age": np.float64}, )
#TRAIN DATA SET
#Print to standard output, and see the results in the "log" section below af... | 3 8 8 |
import numpy as np
import pandas as pd
#Print you can execute arbitrary python code
train = pd.read_csv("../input/train.csv", dtype={"Age": np.float64}, )
test = pd.read_csv("../input/test.csv", dtype={"Age": np.float64}, )
#TRAIN DATA SET
#Print to standard output, and see the results in the "log" section below af... | 3 7 8 |
# This script shows you how to make a submission using a few
# useful Python libraries.
# It gets a public leaderboard score of 0.76077.
# Maybe you can tweak it and do better...?
import pandas as pd
import xgboost as xgb
from sklearn.preprocessing import LabelEncoder
import numpy as np
# Load the data
train_df = pd.... | 5 9 9 |
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import seaborn as sns
train = pd.read_csv("../input/train.csv", dtype={"Age": np.float64}, )
# Replacing missing ages with median
train["Age"][np.isnan(train["Age"])] = np.median(train["Age"])
train["Survived"][train["Survived"]==1] = "Survived"
t... | 4 8 9 |
import pandas as pd
import os
os.system("ls ../input")
train = pd.read_csv("../input/train.csv")
print("Training set has {0[0]} rows and {0[1]} columns".format(train.shape))
print(train.head()) | 3 8 9 |
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import seaborn as sns
train = pd.read_csv("../input/train.csv", dtype={"Age": np.float64}, )
# Replacing missing ages with median
train[np.isnan(train["Age"]), "Age"] = np.median(train["Age"])
plt.figure()
train
sns.pairplot(data=train[["Fare","S... | 3 7 8 |
print("Rate Limit Test")
| 10 10 10 |
import matplotlib
matplotlib.use("Agg")
import matplotlib.pyplot as plt; plt.rcdefaults()
import numpy as np
import pandas as pd
train = pd.read_csv("../input/train.csv")
train["TitleLength"] = train.Title.apply(len)
plt.hist([train[train.OpenStatus==0].TitleLength.values,
train[train.OpenStatus==1].TitleLe... | 6 9 8 |
#This script looks at the survival rate for those with the most common names
import numpy as np
import pandas as pd
train = pd.read_csv("../input/train.csv", dtype={"Age": np.float64}, )
dfTitanic = train
#Extract the first name from passenger name
dfTitanic['FirstName'] = dfTitanic['Name'].str.extract('(Mr\. |Miss\... | 5 7 8 |
print("Rate Limit Test")
| 10 10 10 |
"""
Simple demo of a horizontal bar chart.
"""
import matplotlib
matplotlib.use("Agg")
import matplotlib.pyplot as plt; plt.rcdefaults()
import numpy as np
import pandas as pd
train = pd.read_csv("../input/train.csv", parse_dates=["datetime"])
train["hour"] = pd.DatetimeIndex(train['datetime']).hour
train["temp"] = t... | 3 8 9 |
import matplotlib
matplotlib.use("Agg")
import matplotlib.pyplot as plt; plt.rcdefaults()
import numpy as np
import pandas as pd
train = pd.read_csv("../input/train.csv")
train.head()
train.head().transpose()
train["TitleLength"] = train.Title.apply(len)
import matplotlib.pyplot as plt
import numpy as np
plt.hist(tr... | 3 5 7 |
import numpy as np
import pandas as pd
#Print you can execute arbitrary python code
train = pd.read_csv("../input/train.csv", dtype={"Age": np.float64}, )
test = pd.read_csv("../input/test.csv", dtype={"Age": np.float64}, )
#TRAIN DATA SET
#Print to standard output, and see the results in the "log" section below af... | 3 8 8 |
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import seaborn as sns
train = pd.read_csv("../input/train.csv", parse_dates=["datetime"])
train["hour"] = pd.DatetimeIndex(train['datetime']).hour
train["month"] = pd.DatetimeIndex(train['datetime']).month
train["temp"] = train.temp*9.0/5.0+32.0
t... | 6 8 9 |
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import seaborn as sns
train = pd.read_csv("../input/train.csv", parse_dates=["datetime"])
train["hour"] = pd.DatetimeIndex(train['datetime']).hour
train["month"] = pd.DatetimeIndex(train['datetime']).month
train["temp"] = train.temp*9.0/5.0+32.0
t... | 8 9 9 |
import matplotlib
matplotlib.use("Agg")
import matplotlib.pyplot as plt
import statsmodels.api as sm
import statsmodels.formula.api as smf
import numpy as np
import math
##AUTOMATICLY! LOVELY!!
########################
import pandas as pd #<--- pitty! 0.14.1 when 0.16 is already there!!
import sklearn as sk
def get_f... | 4 7 7 |
print("Rate Limit Test")
| 10 10 10 |
import csv
from collections import defaultdict
from math import log10
import matplotlib
matplotlib.use("Agg") #what?
import matplotlib.pyplot as plt
#Get counts for starting digit in columns: counts, registered, casual
d = defaultdict(int)
for e, row in enumerate(csv.DictReader(open("../input/train.csv"))):
d[row["co... | |
"""
Simple demo of a horizontal bar chart.
"""
import matplotlib
matplotlib.use("Agg")
import matplotlib.pyplot as plt; plt.rcdefaults()
import numpy as np
import pandas as pd
train = pd.read_csv("../input/train.csv", parse_dates=["datetime"])
train["hour"] = pd.DatetimeIndex(train['datetime']).hour
train["month"] = p... | 6 8 9 |
import sys
import numpy as np
from sklearn import cross_validation
import pandas as pd
import csv
from sklearn.grid_search import GridSearchCV
from sklearn.decomposition import RandomizedPCA
from sklearn.svm import SVC
def targetFeatureSplit( data ):
target = []
features = []
for item in data:
targ... | |
import numpy as np
import pandas as pd
train = pd.read_csv("../input/train.csv", dtype={"Age": np.float64}, )
dfTitanic = train
dfTitanic['Male'] = dfTitanic['Sex'] == 'male'
dfTitanic['Name'].head(10)
dfTitanic['FirstName'] = dfTitanic['Name'].str.extract('(Mr\. |Miss\. |Master. |Mrs\.[A-Za-z ]*\()([A-Za-z]*)')[1]
a... | 6 8 9 |
import matplotlib
matplotlib.use("Agg")
import matplotlib.pyplot as plt
import statsmodels.api as sm
import statsmodels.formula.api as smf
import numpy as np
import math
##AUTOMATICLY! LOVELY!!
########################
import pandas as pd #<--- pitty! 0.14.1 when 0.16 is already there!!
import sklearn as sk
def get_f... | |
print("Rate Limit Test")
| 10 10 10 |
import matplotlib
matplotlib.use("Agg")
import matplotlib.pyplot as plt
import statsmodels.api as sm
import statsmodels.formula.api as smf
import numpy as np
import math
##AUTOMATICLY! LOVELY!!
########################
import pandas as pd #<--- pitty! 0.14.1 when 0.16 is already there!!
import sklearn as sk
def get_f... | |
import matplotlib
matplotlib.use("Agg")
import matplotlib.pyplot as plt; plt.rcdefaults()
import numpy as np
import pandas as pd
train = pd.read_csv("../input/train.csv")
plt.hist([train[train.OpenStatus==0].ReputationAtPostCreation.values,train[train.OpenStatus==1].ReputationAtPostCreation.values],label = [0, 1],alp... | 4 8 9 |
print("Rate Limit Test")
| 10 10 10 |
import matplotlib
matplotlib.use("Agg")
import statsmodels.api as sm
import statsmodels.formula.api as smf
import numpy as np
##AUTOMATICLY! LOVELY!!
########################
import pandas as pd #<--- pitty! 0.14.1 when 0.16 is already there!!
import sklearn as sk
def get_feature_mat(fname):
#feature engineering in ... | 5 7 6 |
#This script looks at the survival rate for those with the most common names
import numpy as np
import pandas as pd
train = pd.read_csv("../input/train.csv", dtype={"Age": np.float64}, )
dfTitanic = train
#Extract the first name from passenger name
dfTitanic['FirstName'] = dfTitanic['Name'].str.extract('(Mr\. |Miss\... | 8 8 9 |
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
train = pd.read_csv("../input/train.csv", parse_dates=["datetime"])
train["hour"] = pd.DatetimeIndex(train['datetime']).hour
train["month"] = pd.DatetimeIndex(train['datetime']).month
train["temp"] = train.temp*9.0/5.0+32.0
train["temp_jittered"] ... | |
import numpy as np
import pandas as pd
train = pd.read_csv("../input/train.csv", dtype={"Age": np.float64}, )
dfTitanic = train
dfTitanic['Male'] = dfTitanic['Sex'] == 'male'
dfTitanic['Name'].head(10)
dfTitanic['FirstName'] = dfTitanic['Name'].str.extract('(Mr\. |Miss\. |Master. |Mrs\.[A-Za-z ]*\()([A-Za-z]*)')[1]
p... | 5 8 9 |
import matplotlib
matplotlib.use("Agg")
import statsmodels.api as sm
import statsmodels.formula.api as smf
import numpy as np
##AUTOMATICLY! LOVELY!!
########################
import pandas as pd #<--- pitty! 0.14.1 when 0.16 is already there!!
import sklearn as sk
def get_feature_mat(fname):
#feature engineering in ... | 6 7 6 |
import matplotlib
matplotlib.use("Agg")
import matplotlib.pyplot as plt; plt.rcdefaults()
import numpy as np
import pandas as pd
train = pd.read_csv("../input/train.csv")
plt.hist([train[train.OpenStatus==0].ReputationAtPostCreation.values,
train[train.OpenStatus==1].ReputationAtPostCreation.values],
... | |
"""
Simple demo of a horizontal bar chart.
"""
import matplotlib
matplotlib.use("Agg")
import matplotlib.pyplot as plt; plt.rcdefaults()
import numpy as np
import pandas as pd
train = pd.read_csv("../input/train.csv", parse_dates=["datetime"])
train["hour"] = pd.DatetimeIndex(train['datetime']).hour
train["temp"] = t... | 3 8 9 |
import matplotlib
matplotlib.use("Agg")
import statsmodels.api as sm
import statsmodels.formula.api as smf
import numpy as np
##AUTOMATICLY! LOVELY!!
########################
import pandas as pd #<--- pitty! 0.14.1 when 0.16 is already there!!
import sklearn as sk
def get_feature_mat(fname):
#feature engineering in ... | |
import sys
import pip
import warnings
import seaborn # :)
warnings.filterwarnings("ignore") # only once, I promise.
print(sys.version_info)
print()
for available_distro in sorted(["%s==%s" % (i.key, i.version) for i in pip.get_installed_distributions()]):
print(available_distro)
help('modules') | 7 8 9 |
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import seaborn as sns
train = pd.read_csv("../input/train.csv", dtype={"Age": np.float64}, )
# Replacing missing ages with median
train["Age"][np.isnan(train["Age"])] = np.median(train["Age"])
train["Survived"][train["Survived"]==1] = "Survived"
t... | 6 8 10 |
import numpy as np
import pandas as pd
train = pd.read_csv("../input/train.csv", dtype={"Age": np.float64}, )
dfTitanic = train
dfTitanic['FirstName'] = dfTitanic['Name'].str.extract('(Mr\. |Miss\. |Master. |Mrs\.[A-Za-z ]*\()([A-Za-z]*)')[1]
ax = dfTitanic['FirstName'].value_counts().head(20).plot(kind='bar',fontsiz... | 7 8 9 |
#This script looks at the survival rate for those with the most common names
import numpy as np
import pandas as pd
train = pd.read_csv("../input/train.csv", dtype={"Age": np.float64}, )
dfTitanic = train
#Extract the first name from passenger name
dfTitanic['FirstName'] = dfTitanic['Name'].str.extract('(Mr\. |Miss\... | 4 7 8 |
#This script looks at the survival rate for those with the most common names
import numpy as np
import pandas as pd
train = pd.read_csv("../input/train.csv", dtype={"Age": np.float64}, )
dfTitanic = train
#Extract the first name from passenger name
dfTitanic['FirstName'] = dfTitanic['Name'].str.extract('(Mr\. |Miss\... | 5 8 8 |
print("Rate Limit Test")
| 10 10 10 |
from theano import function, config, shared, sandbox
import theano.tensor as T
import numpy
import time
vlen = 10 * 30 * 768 # 10 x #cores x # threads per core
iters = 1000
rng = numpy.random.RandomState(22)
x = shared(numpy.asarray(rng.rand(vlen), config.floatX))
f = function([], T.exp(x))
t0 = time.time()
for i i... | |
print("Rate Limit Test")
| 10 10 10 |
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
train = pd.read_csv("../input/train.csv", parse_dates=["datetime"])
train["hour"] = pd.DatetimeIndex(train['datetime']).hour
train["month"] = pd.DatetimeIndex(train['datetime']).month
train["temp"] = train.temp*9.0/5.0+32.0
train["temp_jittered"] ... | 5 8 9 |
import numpy as np
import pandas as pd
import pylab as Plb
#Print you can execute arbitrary python code
train = pd.read_csv("../input/train.csv", dtype={"Age": np.float64}, )
test = pd.read_csv("../input/test.csv", dtype={"Age": np.float64}, )
#TRAIN DATA SET
#Print to standard output, and see the results in the "l... | 7 9 8 |
print("Rate Limit Test")
| 10 10 10 |
#This script looks at the survival rate for those with the most common names
import numpy as np
import pandas as pd
train = pd.read_csv("../input/train.csv", dtype={"Age": np.float64}, )
dfTitanic = train
#Extract the first name from passenger name
dfTitanic['FirstName'] = dfTitanic['Name'].str.extract('(Mr\. |Miss\... | |
import csv
from collections import defaultdict
from math import log10
import matplotlib.pyplot as plt
#Get counts for starting digit in columns: counts, registered, casual
d = defaultdict(int)
for e, row in enumerate(csv.DictReader(open("../input/train.csv"))):
d[row["count"][0]] += 1
d[row["registered"][0]] += 1
d... | |
print("Rate Limit Test")
| 10 10 10 |
#This script looks at the survival rate for those with the most common names
import numpy as np
import pandas as pd
train = pd.read_csv("../input/train.csv", dtype={"Age": np.float64}, )
dfTitanic = train
#Extract the first name from passenger name
dfTitanic['FirstName'] = dfTitanic['Name'].str.extract('(Mr\. |Miss\... | |
print("Rate Limit Test")
| 10 10 10 |
#This script looks at the survival rate for those with the most common names
import numpy as np
import pandas as pd
train = pd.read_csv("../input/train.csv", dtype={"Age": np.float64}, )
dfTitanic = train
#Extract the first name from passenger name
dfTitanic['FirstName'] = dfTitanic['Name'].str.extract('(Mr\. |Miss\... | 6 8 9 |
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
train = pd.read_csv("../input/train.csv", parse_dates=["datetime"])
train["hour"] = pd.DatetimeIndex(train['datetime']).hour
train["month"] = pd.DatetimeIndex(train['datetime']).month
train["temp"] = train.temp*9.0/5.0+32.0
train["temp_jittered"] ... | 6 8 8 |
import numpy as np
import pandas as pd
#Print you can execute arbitrary python code
train = pd.read_csv("../input/train.csv", dtype={"Age": np.float64}, )
test = pd.read_csv("../input/test.csv", dtype={"Age": np.float64}, )
#Print to standard output, and see the results in the "log" section below after running your s... | 5 10 10 |
print("Rate Limit Test")
| 10 10 10 |
import pandas as pd
from lasagne import layers
from lasagne.nonlinearities import softmax, rectify
from lasagne.updates import nesterov_momentum
from nolearn.lasagne import NeuralNet
import numpy as np
def fit_convolutional_model(reshaped_train_x, y, image_width, image_height, reshaped_test_x):
"""Convolutional ... | 7 9 9 |
import numpy as np
import pandas as pd
import pylab as Plb
#Print you can execute arbitrary python code
train = pd.read_csv("../input/train.csv", dtype={"Age": np.float64}, )
test = pd.read_csv("../input/test.csv", dtype={"Age": np.float64}, )
#TRAIN DATA SET
#Print to standard output, and see the results in the "l... | |
import numpy as np
import pandas as pd
#Print you can execute arbitrary python code
train = pd.read_csv("../input/train.csv", dtype={"Age": np.float64}, )
test = pd.read_csv("../input/test.csv", dtype={"Age": np.float64}, )
#Print to standard output, and see the results in the "log" section below after running your s... | 5 9 9 |
import numpy as np
import pandas as pd
#Print you can execute arbitrary python code
train = pd.read_csv("../input/train.csv", dtype={"Age": np.float64}, )
test = pd.read_csv("../input/test.csv", dtype={"Age": np.float64}, )
#TRAIN DATA SET
#Print to standard output, and see the results in the "log" section below af... | 5 8 9 |
#This script looks at the survival rate for those with the most common names
import numpy as np
import pandas as pd
train = pd.read_csv("../input/train.csv", dtype={"Age": np.float64}, )
dfTitanic = train
#Extract the first name from passenger name
dfTitanic['FirstName'] = dfTitanic['Name'].str.extract('(Mr\. |Miss\... | 4 7 9 |
import matplotlib
import matplotlib.pyplot as plt
| 10 10 10 |
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import seaborn as sns
train = pd.read_csv("../input/train.csv", dtype={"Age": np.float64}, )
# Replacing missing ages with median
train["Age"][np.isnan(train["Age"])] = np.median(train["Age"])
train["Survived"][train["Survived"]==1] = "Survived"
t... | 4 8 9 |
import matplotlib
matplotlib.use("Agg")
import matplotlib.pyplot as plt; plt.rcdefaults()
import numpy as np
import pandas as pd
train = pd.read_csv("../input/train.csv")
plt.hist([train[train.OpenStatus==0].ReputationAtPostCreation.values,
train[train.OpenStatus==1].ReputationAtPostCreation.values],
... | 4 9 8 |
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import seaborn as sns
train = pd.read_csv("../input/train.csv", parse_dates=["datetime"])
train["hour"] = pd.DatetimeIndex(train['datetime']).hour
train["month"] = pd.DatetimeIndex(train['datetime']).month
train["temp"] = train.temp*9.0/5.0+32.0
t... | 6 8 9 |
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import seaborn as sns
train = pd.read_csv("../input/train.csv", dtype={"Age": np.float64})
print(train.head())
plt.figure()
print(np.max(train["Age"]))
print(np.min(train["Age"]))
print(np.range(train["Age"]))
sns.pairplot(data=train[["Fare","Survi... | 4 5 9 |
import matplotlib
matplotlib.use("Agg")
import statsmodels.api as sm
import statsmodels.formula.api as smf
import numpy as np
##AUTOMATICLY! LOVELY!!
########################
import pandas as pd #<--- pitty! 0.14.1 when 0.16 is already there!!
import sklearn as sk
def get_feature_mat(fname):
#feature engineering in ... | 3 6 5 |
import numpy as np
import pandas as pd
#Print you can execute arbitrary python code
train = pd.read_csv("../input/train.csv", dtype={"Age": np.float64}, )
test = pd.read_csv("../input/test.csv", dtype={"Age": np.float64}, )
#TRAIN DATA SET
#Print to standard output, and see the results in the "log" section below af... | 4 8 10 |
import numpy as np
import pandas as pd
train = pd.read_csv("../input/train.csv", dtype={"Age": np.float64}, )
dfTitanic = train
dfTitanic['Male'] = dfTitanic['Sex'] == 'male'
dfTitanic['Name'].head(10)
dfTitanic['FirstName'] = dfTitanic['Name'].str.extract('(Mr\. |Miss\. |Master. |Mrs\.[A-Za-z ]*\()([A-Za-z]*)')[1]
a... | |
#This script looks at the survival rate for those with the most common names
import numpy as np
import pandas as pd
train = pd.read_csv("../input/train.csv", dtype={"Age": np.float64}, )
dfTitanic = train
#Extract the first name from passenger name
dfTitanic['FirstName'] = dfTitanic['Name'].str.extract('(Mr\. |Miss\... | |
print("Rate Limit Test")
| 10 10 10 |
import numpy as np
import pandas as pd
#Print you can execute arbitrary python code
train = pd.read_csv("../input/train.csv", dtype={"Age": np.float64}, )
test = pd.read_csv("../input/test.csv", dtype={"Age": np.float64}, )
#TRAIN DATA SET
#Print to standard output, and see the results in the "log" section below af... | |
# Some fancy Python:
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import seaborn as sns
df = pd.DataFrame({"A":[1,2,3,4,5,6],"B":[4,7,1,2,3,4]})
plt.figure()
sns.pairplot(data=df)
plt.savefig("x.png") | 10 10 10 |
from keras.models import Sequential
from keras.utils import np_utils
from keras.layers.core import Dense, Activation, Dropout
import pandas as pd
import numpy as np
# Read data
train = pd.read_csv('../input/train.csv')
labels = train.ix[:,0].values.astype('int32')
X_train = (train.ix[:,1:].values).astype('float32')
X... | 7 9 9 |
import matplotlib
matplotlib.use("Agg")
import matplotlib.pyplot as plt
import statsmodels.api as sm
import statsmodels.formula.api as smf
import numpy as np
import math
##AUTOMATICLY! LOVELY!!
########################
import pandas as pd #<--- pitty! 0.14.1 when 0.16 is already there!!
import sklearn as sk
def get_f... | 5 6 7 |
"""
Simple demo of a horizontal bar chart.
"""
import matplotlib
matplotlib.use("Agg")
import matplotlib.pyplot as plt; plt.rcdefaults()
import numpy as np
import pandas as pd
train = pd.read_csv("../input/train.csv", parse_dates=["datetime"])
train["hour"] = pd.DatetimeIndex(train['datetime']).hour
train["temp"] = t... | |
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import seaborn as sns
train = pd.read_csv("../input/train.csv", dtype={"Age": np.float64}, )
# Replacing missing ages with median
train["Age"][np.isnan(train["Age"])] = np.median(train["Age"])
train["Survived"][train["Survived"]==1] = "Survived"
t... | 6 8 10 |
print("Rate Limit Test")
| 10 10 10 |
#This script looks at the survival rate for those with the most common names
import numpy as np
import pandas as pd
train = pd.read_csv("../input/train.csv", dtype={"Age": np.float64}, )
dfTitanic = train
#Extract the first name from passenger name
dfTitanic['FirstName'] = dfTitanic['Name'].str.extract('(Mr\. |Miss\... |
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