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PYTHON
drop a column from dataframe
#To delete the column without having to reassign df
df.drop('column_name', axis=1, inplace=True)
delete column pandas dataframe
df.drop(columns='column_name', inplace=True)
remove column from dataframe
df.drop('column_name', axis=1, inplace=True)
drop a column in pandas
note: df is your dataframe
df = df.drop('coloum_name',axis=1)
how to drop a column by name in pandas
>>> df.drop(columns=['B', 'C'])
A D
0 0 3
1 4 7
2 8 11
python - drop a column
# 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)
drop a column from dataframe
df = df.drop('column_name', 1)
drop column dataframe
df.drop(columns=['Unnamed: 0'])
delete unnamed coloumns in pandas
# Best method so far.
df = df.loc[:, ~df.columns.str.contains('^Unnamed')]
pandas drop column by name
df.drop(columns=['Column_Name1','Column_Name2'], axis=1, inplace=True)
drop a column from dataframe
#working with "text" syntax for the columns:
df.drop(['column_nameA', 'column_nameB'], axis=1, inplace=True)
pandas delete column by name
df = df.drop('column_name', axis=1)
pandas remove column
delete columns pandas
df = df.drop(df.columns[[0, 1, 3]], axis=1) # df.columns is zero-based pd.Index
df.drop(['column_nameA', 'column_nameB'], axis=1, inplace=True)
drop a column in pandas
df = df.drop(df.columns[[0, 1, 3]], axis=1) # df.columns is zero-based pd.Index
pandas dataframe delete column
how to drop a column in python
# 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)
delete pandas column
how to delete a column from a dataframe in python
remove a column from dataframe
del df['column_name'] #to remove a column from dataframe
delete columns pandas
df = df.drop(df.columns[[0, 1, 3]], axis=1)
pandas drop column in dataframe
>>> df.drop(['B', 'C'], axis=1)
A D
0 0 3
1 4 7
2 8 11
drop column from dataframe
var = dataframe.drop(['col', 'col'], axis=1)
var.sum()
remove columns from a dataframe python
# Import pandas package
import 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
delete a column in pandas
# Remove the unwanted columns
data.drop(['Country code', 'Continental region'], axis=1, inplace=True)
data.head()
remove columns from dataframe
df.drop('col_name',1) #1 drop column / 0 drop row
delete column in dataframe pandas
df = df.drop(df.columns[[0, 1, 3]], axis=1) # df.columns is zero-based pd.Index
remove columns that start with pandas
cols = [c for c in df.columns if c.lower()[:6] != 'string']
df=df[cols]
Python Delete column
import pandas as pd
EmployeeData=pd.DataFrame({'Name': ['ram','ravi','sham','sita','gita'],
'id': [101,102,103,104,105],
'Gender': ['M','M','M','F','F'],
'Age': [21,25,24,28,25]
})
# Priting data
print(EmployeeData)
# Deleting few columns
DeleteList=['Name','Gender']
EmployeeData=EmployeeData.drop(DeleteList, axis=1)
# Priting data
print(EmployeeData)
how to delete a column in pandas dataframe
delete column from pandas data frame
how to drop a column by name in pandas
>>> midx = pd.MultiIndex(levels=[['lama', 'cow', 'falcon'],
... ['speed', 'weight', 'length']],
... codes=[[0, 0, 0, 1, 1, 1, 2, 2, 2],
... [0, 1, 2, 0, 1, 2, 0, 1, 2]])
>>> df = pd.DataFrame(index=midx, columns=['big', 'small'],
... data=[[45, 30], [200, 100], [1.5, 1], [30, 20],
... [250, 150], [1.5, 0.8], [320, 250],
... [1, 0.8], [0.3, 0.2]])
>>> df
big small
lama speed 45.0 30.0
weight 200.0 100.0
length 1.5 1.0
cow speed 30.0 20.0
weight 250.0 150.0
length 1.5 0.8
falcon speed 320.0 250.0
weight 1.0 0.8
length 0.3 0.2
remove a columns in pandas
DataFrame.drop(self, labels=None, axis=0, index=None, columns=None, level=None, inplace=False, errors='raise')[source]
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