Tag: pandas

5 ways to apply an IF condition in pandas DataFrame

from Need to apply an IF condition in Pandas DataFrame? If so, in this tutorial, you’ll see 5 different ways to apply such a condition. Specifically, you’ll see how to apply an IF condition for: Set of numbers Set of numbers and lambda Strings Strings and lambada OR condition Applying an IF condition in Pandas DataFrame… Continue reading 5 ways to apply an IF condition in pandas DataFrame

Selecting Subsets of Data in Pandas

from What Code single column  df[‘food’] multiple columns df[[‘color’, ‘food’, ‘score’]] single row df.loc[‘Niko’] multiple rows df.loc[[‘Niko’, ‘Penelope’]] slice notation to select a range of rows df.loc[‘Niko’:’Dean’]   df.loc[:’Aaron’] stepping by 2 df.loc[‘Niko’:’Christina’:2] rows and columns df.loc[row_selection, column_selection]   df.loc[‘Jane’:’Penelope’, [‘state’, ‘color’]] single row df.iloc[3] multiple rows df.iloc[[5, 2, 4]]   df.iloc[3:5]   df.iloc[[2,3], [0,… Continue reading Selecting Subsets of Data in Pandas

python, Pandas Categorize the range

df[‘PriceBin’] = pd.cut(df[‘PriceAvg’], bins = 3)df[‘PriceBin’].value_counts() (54060.0, 2040000.0] 209 (2040000.0, 4020000.0] 4 (4020000.0, 6000000.0] 1 Name: PriceBin, dtype: int64 df[‘PriceBin’] = pd.qcut(df[‘PriceAvg’], q=3) df[‘PriceBin’].value_counts().sort_index() (59999.999, 210000.0] 77 (210000.0, 315000.0] 66 (315000.0, 6000000.0] 71 Name: PriceBin, dtype: int64 PriceBin SalesAvg0(59999.999, 210000.0] 42.0000001(210000.0, 315000.0] 145.1666672(315000.0, 6000000.0] 114.200000