pandas select column by index
# A B C # 0 1 3 5 # 1 2 4 6 column_B = a_dataframe.iloc[:, 1] print(column_B) # OUTPUT # 0 3 # 1 4
pandas select column by index
# A B C # 0 1 3 5 # 1 2 4 6 column_B = a_dataframe.iloc[:, 1] print(column_B) # OUTPUT # 0 3 # 1 4
select rows from dataframe pandas
from pandas import DataFrame boxes = {'Color': ['Green','Green','Green','Blue','Blue','Red','Red','Red'], 'Shape': ['Rectangle','Rectangle','Square','Rectangle','Square','Square','Square','Rectangle'], 'Price': [10,15,5,5,10,15,15,5] } df = DataFrame(boxes, columns= ['Color','Shape','Price']) select_color = df.loc[df['Color'] == 'Green'] print (select_color)
select columns pandas
df1 = df.iloc[:,0:2] # Remember that Python does not slice inclusive of the ending index.
loc and iloc in pandas
iloc - default indexes (system generated) loc - table indexes or we manually given indexes
retrieve row by index pandas
rowData = dfObj.loc[ 'b' , : ]
add an index column in range dataframe
df = df.loc[df.index.repeat(df['a'])] df['c'] = df.groupby(level=0).cumcount() + 1 df = df.reset_index(drop=True) print (df) a b c 0 1 x 1 1 2 y 1 2 2 y 2 3 3 z 1 4 3 z 2 5 3 z 3
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