python code to drop columns from dataframe
# 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 columnsdrop a column in pandas
note: df is your dataframe
df = df.drop('coloum_name',axis=1)drop list of columns pandas
df.drop([item for item in total_cols if item not in columns_in_use], axis=)python - dataframe columns is a list - drop
# Df with a coulmn (dims) that contain list
key1 = 'site channel fiscal_week'.split()
key2 = 'site dude fiscal_week'.split()
key3 = 'site eng fiscal_week'.split()
keys = pd.DataFrame({'key': [1,2,3],
'dims': [key1,key2,key3]})
# Output
dims key
[site, channel, fiscal_week] 1
[site, dude, fiscal_week] 2
[site, eng, fiscal_week] 3
# Solution
keys['reduced_dims'] = keys['dims'].apply(
lambda row: [val for val in row if val != 'fiscal_week']
)drop dataframe columns from list of column names
# 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)
dfCopyright © 2021 Codeinu
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