Tag: Pandas

Snippet 1 # Select rows 0, 1, 2 (row 3 is not selected) surveys_df[0:3] Snippet 2 # iloc[row slicing, column slicing] surveys_df.iloc[0:3, 1:4] Copyright © Code Fetcher 2020    

Snippet 1 Specifically, .loc[] allows you to select all rows with an index lexicographically using slice notation. This works only if the index is sorted (.sort_index()). Copyright © Code Fetcher 2020    

Snippet 1 sum_column = df[“col1”] + df[“col2”] Copyright © Code Fetcher 2020    

Snippet 1 data[“Gender”].fillna(“No Gender”, inplace = True) Snippet 2 In [7]: df Out[7]: 0 1 0 NaN NaN 1 -0.494375 0.570994 2 NaN NaN 3 1.876360 -0.229738 4 NaN NaN In [8]: df.fillna(0) Out[8]: 0 1 0 0.000000 0.000000 1 -0.494375 0.570994 2 0.000000 0.000000 3 1.876360 -0.229738 4 0.000000 0.000000 Snippet 3 df.replace(np.nan,0) Snippet… Continue reading Pandas Replace Nan Code Example

dimRegion2 = pd.io.sql.get_schema(dimRegion2.reset_index(),’dimRegion2′) source movie_overview_2022 = movie_overview_2022.reset_index(drop=True) source seasons_2022_2013.reset_index(drop = True, inplace = True) source submission = pd.read_csv(“submission_lgbm.csv”, header=None).reset_index() source Copyright © Code Fetcher 2022    

Snippet 1 df.loc[:, df.columns != ‘b’] a c d 0 0.561196 0.013768 0.772827 1 0.882641 0.615396 0.075381 2 0.368824 0.651378 0.397203 3 0.788730 0.568099 0.869127 Copyright © Code Fetcher 2020    

Snippet 1 # Create variable with TRUE if nationality is USA american = df[‘nationality’] == “USA” # Create variable with TRUE if age is greater than 50 elderly = df[‘age’] > 50 # Select all cases where nationality is USA and age is greater than 50 df[american & elderly] Snippet 2 >>> df[“A”][(df[“B”] > 50)… Continue reading Pandas Select Rows By Multiple Conditions Code Example

Snippet 1 df.head(10) Copyright © Code Fetcher 2020    

Snippet 1 #UPDATED (June 2020): Introduced in Pandas 0.25.0, #Pandas has added new groupby behavior “named aggregation” and tuples, #for naming the output columns when applying multiple aggregation functions #to specific columns. df.groupby( [‘col1′,’col2’] ).agg( sum_col3 = (‘col3′,’sum’), sum_col4 = (‘col4′,’sum’), ).reset_index() Snippet 2 df.groupby([‘col1′,’col2’]).agg({‘col3′:’sum’,’col4′:’sum’}).reset_index() Similar Snippets Pandas Ttable With Sum Totals Code Example –… Continue reading Pandas Sum Multiple Columns Groupby Code Example

Snippet 1 dfObj.isnull().sum() Snippet 2 import numpy as np import pandas as pd df = pd.DataFrame({‘a’: [10,20],’b’:[100,200],’c’: [‘a’,’b’]}) df.loc[‘Column_Total’]= df.sum(numeric_only=True, axis=0) df.loc[:,’Row_Total’] = df.sum(numeric_only=True, axis=1) print(df) a b c Row_Total 0 10.0 100.0 a 110.0 1 20.0 200.0 b 220.0 Column_Total 30.0 300.0 NaN 330.0 Copyright © Code Fetcher 2020