to filter columns 0 and 1 from the DataFrame.

df3 = df1.filter([0, 1], axis=1)

Here, axis=1 parameter indicates that we are filtering columns.

So, the output of the above program will be:

df1: 
    0   1   2   3   4
0   1   2   3   4   5
1   6   7   8   9  10
2  11  12  13  14  15
3  16  17  18  19  20
df2: 
    0   1   2   3   4
0   1   2   3   4   5
1   6   7   8   9  10
3  16  17  18  19  20
df3: 
    0   1
0   1   2
1   6   7
2  11  12
3  16  17

Please note that if the rows and columns of the DataFrame are labeled, then we can use regular expressions to filter those rows and columns. Let’s look at an example:

import pandas

df4 = pandas.read_csv("iris.csv")
print(df4.head())
df5 = df4.filter(like="length", axis=1)
print("df5: \n", df5.head())

df6 = df4.filter(regex="^petal", axis=1)
print("df6: \n", df6.head())

Here, we are first reading the iris dataset from a CSV file. After that, we are using the following Python statement to filter all columns that contain the string “length”.

df5 = df4.filter(like="length", axis=1)

Next, we are using the following Python statement to filter the columns that match the regular expression “^petal”. In other words, we are filtering all columns whose labels start with the string “petal”.

df6 = df4.filter(regex="^petal", axis=1)

The output of the above program will be:

   sepal_length  sepal_width  petal_length  petal_width species
0           5.1          3.5           1.4          0.2  setosa
1           4.9          3.0           1.4          0.2  setosa
2           4.7          3.2           1.3          0.2  setosa
3           4.6          3.1           1.5          0.2  setosa
4           5.0          3.6           1.4          0.2  setosa
df5: 
    sepal_length  petal_length
0           5.1           1.4
1           4.9           1.4
2           4.7           1.3
3           4.6           1.5
4           5.0           1.4
df6: 
    petal_length  petal_width
0           1.4          0.2
1           1.4          0.2
2           1.3          0.2
3           1.5          0.2
4           1.4          0.2