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We may face some problems when extracting multiple columns of data from Pandas DataFrame, mainly because they treat Dataframe as a two-dimensional array. To select multiple columns of data from a DataFrame, we can use basic indexing methods, pass a list of column names to __getitem__the syntax ( ), or use the and methods []provided by the Pandas library . In this tutorial, we will select multiple columns from the following DataFrame.iloc()loc()

Example DataFrame:

import pandas as pd
import numpy as np

df = pd.DataFrame(np.random.rand(4, 4), columns=["a", "b", "c", "d"])

print(df)

Output:

          a         b         c         d
0  0.255086  0.282203  0.342223  0.263599
1  0.744271  0.591687  0.861554  0.871859
2  0.420066  0.713664  0.770193  0.207427
3  0.014447  0.352515  0.535801  0.119759

Use __getitem__the syntax ( []) to select multiple columns

[]We can select multiple columns from a DataFrame by storing the column names to be extracted in a list and then passing it to . The following code will explain how we can select the columns aand from the DataFrame shown previously c.

import pandas as pd
import numpy as np

df = pd.DataFrame(np.random.rand(4, 4), columns=["a", "b", "c", "d"])

print(df[["a", "c"]])

Output:

          a         c
0  0.255086  0.342223
1  0.744271  0.861554
2  0.420066  0.770193
3  0.014447  0.535801

Selecting Multiple Columns in Pandas Using the iloc()and Methodsloc()

We can also use the iloc()and loc()methods to select multiple columns.

When we want to extract them using column index, we can use iloc()as shown in the following example.

import pandas as pd
import numpy as np

df = pd.DataFrame(np.random.rand(4, 4), columns=["a", "b", "c", "d"])
print(df.iloc[:, [0, 2]])

Output:

          a         c
0  0.255086  0.342223
1  0.744271  0.861554
2  0.420066  0.770193
3  0.014447  0.535801

Similarly, when we want to select columns using column names, we can use loc()as shown in the following image.

import pandas as pd
import numpy as np

df = pd.DataFrame(np.random.rand(4, 4), columns=["a", "b", "c", "d"])

print(df.loc[:, ["a", "c"]])

Output:

          a         c
0  0.255086  0.342223
1  0.744271  0.861554
2  0.420066  0.770193
3  0.014447  0.535801