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import pandas as pd | |
import streamlit as st | |
from pandas.api.types import ( | |
is_categorical_dtype, | |
is_datetime64_any_dtype, | |
is_numeric_dtype, | |
is_object_dtype | |
) | |
st.title("Week 2 - Filter your Airbnb Listings dataframe!") | |
st.write( | |
"""This app is based on this blog [here](https://blog.streamlit.io/auto-generate-a-dataframe-filtering-ui-in-streamlit-with-filter_dataframe/). | |
Can you think of ways to extend it with visuals? | |
""" | |
) | |
def filter_dataframe(df: pd.DataFrame) -> pd.DataFrame: | |
""" | |
Adds a UI on top of a dataframe to let viewers filter columns | |
Args: | |
df (pd.DataFrame): Original dataframe | |
Returns: | |
pd.DataFrame: Filtered dataframe | |
""" | |
modify = st.checkbox("Add filters") | |
if not modify: | |
return df | |
df = df.copy() | |
# Try to convert datetimes into a standard format (datetime, no timezone) | |
for col in df.columns: | |
if is_object_dtype(df[col]): | |
try: | |
df[col] = pd.to_datetime(df[col]) | |
except Exception: | |
pass | |
if is_datetime64_any_dtype(df[col]): | |
df[col] = df[col].dt.tz_localize(None) | |
modification_container = st.container() | |
with modification_container: | |
to_filter_columns = st.multiselect("Filter dataframe on", df.columns) | |
for column in to_filter_columns: | |
left, right = st.columns((1, 20)) | |
left.write("↳") | |
# Treat columns with < 10 unique values as categorical | |
if is_categorical_dtype(df[column]) or df[column].nunique() < 10: | |
user_cat_input = right.multiselect( | |
f"Values for {column}", | |
df[column].unique(), | |
default=list(df[column].unique()), | |
) | |
df = df[df[column].isin(user_cat_input)] | |
elif is_numeric_dtype(df[column]): | |
_min = float(df[column].min()) | |
_max = float(df[column].max()) | |
step = (_max - _min) / 100 | |
user_num_input = right.slider( | |
f"Values for {column}", | |
_min, | |
_max, | |
(_min, _max), | |
step=step, | |
) | |
df = df[df[column].between(*user_num_input)] | |
elif is_datetime64_any_dtype(df[column]): | |
user_date_input = right.date_input( | |
f"Values for {column}", | |
value=( | |
df[column].min(), | |
df[column].max(), | |
), | |
) | |
if len(user_date_input) == 2: | |
user_date_input = tuple(map(pd.to_datetime, user_date_input)) | |
start_date, end_date = user_date_input | |
df = df.loc[df[column].between(start_date, end_date)] | |
else: | |
user_text_input = right.text_input( | |
f"Substring or regex in {column}", | |
) | |
if user_text_input: | |
df = df[df[column].str.contains(user_text_input)] | |
return df | |
df = pd.read_csv( | |
"WK2_Airbnb_Amsterdam_listings_proj_solution.csv", index_col=0 | |
) | |
st.dataframe(filter_dataframe(df)) | |