[P/M/K] simple way to downcast type to reduce memory

<class 'pandas.core.frame.DataFrame'> RangeIndex: 5 entries, 0 to 4 Data columns (total 11 columns): Season_Year    5 non-null int64 GameKey        5 non-null int64 PlayID         5 non-null int64 GSISID         5 non-null float64 Time           5 non-null object x              5 non-null float64 y              5 non-null float64 dis            5 non-null float64 o              5 non-null float64 dir            5 non-null float64 Event          0 non-null float64 dtypes: float64(7), int64(3), object(1) memory usage: 520.0+ bytes 

# Find out the smallest data type possible for each numeric feature float_cols = df_temp.select_dtypes(include=['float']) int_cols = df_temp.select_dtypes(include=['int'])  for cols in float_cols.columns:     df_temp[cols] = pd.to_numeric(df_temp[cols], downcast='float')      for cols in int_cols.columns:     df_temp[cols] = pd.to_numeric(df_temp[cols], downcast='integer')  print(df_temp.info()) 

<class 'pandas.core.frame.DataFrame'> RangeIndex: 5 entries, 0 to 4 Data columns (total 11 columns): Season_Year    5 non-null int16 GameKey        5 non-null int8 PlayID         5 non-null int16 GSISID         5 non-null float32 Time           5 non-null object x              5 non-null float32 y              5 non-null float32 dis            5 non-null float32 o              5 non-null float32 dir            5 non-null float32 Event          0 non-null float32 dtypes: float32(7), int16(2), int8(1), object(1) memory usage: 285.0+ bytes None 

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