# Let df be a dataframe# Let new_df be a dataframe after dropping a column
new_df = df.drop(labels='column_name', axis=1)# Or if you don't want to change the name of the dataframe
df = df.drop(labels='column_name', axis=1)# Or to remove several columns
df = df.drop(['list_of_column_names'], axis=1)# axis=0 for 'rows' and axis=1 for columns
# axis=1 tells Python that we want to apply function on columns instead of rows# To delete the column permanently from original dataframe df, we can use the option inplace=True
df.drop(['A','B','C'], axis=1, inplace=True)
# axis=1 tells Python that we want to apply function on columns instead of rows# To delete the column permanently from original dataframe df, we can use the option inplace=True
df.drop(['column_1','Column_2'], axis =1, inplace =True)
df.drop(['column_1','Column_2'], axis =1, inplace =True)# Remove all columns between column index 1 to 3
df.drop(df.iloc[:,1:3], inplace =True, axis =1)
# Import pandas packageimport pandas as pd
# create a dictionary with five fields each
data ={'A':['A1','A2','A3','A4','A5'],'B':['B1','B2','B3','B4','B5'],'C':['C1','C2','C3','C4','C5'],'D':['D1','D2','D3','D4','D5'],'E':['E1','E2','E3','E4','E5']}# Convert the dictionary into DataFrame
df = pd.DataFrame(data)#drop the 'A' column from your dataframe df
df.drop(['A'],axis=1,inplace=True)
df
#-->df contains 'B','C','D' and 'E'#in this example you will change your dataframe , if you don't want to ,#just remove the in place parameter and assign your result to an other variable
df1=df.drop(['B'],axis=1)#-->df1 contains 'C','D','E'
df1
df.drop(['Col_1','Col_2'], axis =1)# to drop full colum more general way can visulize easily
df.drop(['Col_1','Col_2'], axis =1, inplace =True)# advanced : to generate df without making copies inside memory
# When you have many columns, and only want to keep a few:# drop columns which are not needed.# df = pandas.Dataframe()
columnsToKeep =['column_1','column_13','column_99']
df_subset = df[columnsToKeep]# Or:
df = df[columnsToKeep]
# Drop The Original Categorical Columns which had Whitespace Issues in their values
df.drop(cat_columns, axis =1, inplace =True)
dict_1 ={'workclass_stripped':'workclass','education_stripped':'education','marital-status_stripped':'marital_status','occupation_stripped':'occupation','relationship_stripped':'relationship','race_stripped':'race','sex_stripped':'sex','native-country_stripped':'native-country','Income_stripped':'Income'}
df.rename(columns = dict_1, inplace =True)
df
import pandas as pd
# create a sample dataframe
data ={'A':['a1','a2','a3'],'B':['b1','b2','b3'],'C':['c1','c2','c3'],'D':['d1','d2','d3']}
df = pd.DataFrame(data)# print the dataframeprint("Original Dataframe:
")print(df)# remove column C
df = df.drop('C', axis=1)print("
After dropping C:
")print(df)