Pandas slice()

import pandas as pd

# create a DataFrame
df = pd.DataFrame({
    'A': [1, 2, 3, 4],
    'B': [5, 6, 7, 8],
    'C': [9, 10, 11, 12]
})

# select rows from index 0 to index 2 (inclusive)
result = df.loc[slice(0, 2)]

print(result)

'''
Output

    A  B   C
0  1  5   9
1  2  6  10
2  3  7  11
'''

slice() Syntax

The syntax of the slice() method in Pandas is:

slice(start, stop, step)

slice() Arguments

The slice() method takes following arguments:

• start (optional) - the starting index or label
• stop (optional) - the stopping index or label. This endpoint is inclusive in Pandas
• step (optional) - the step size for selecting every nth row/column.

slice() Return Value

The slice() method creates a slice object that is used with .loc[] to return a selected subset of a DataFrame.

Example 1: Use slice() to Select Rows

import pandas as pd

# create a DataFrame
df = pd.DataFrame({'A': [1, 2, 3, 4], 
                                  'B': [5, 6, 7, 8]})

# create a slice object for rows
row_slice = slice(1, 3)

# use the slice object with .loc to select a subset of the DataFrame
subset_df = df.loc[row_slice]

print(subset_df)

Output

   A  B
1  2  6
2  3  7
3  4  8

Here, when we use slice(1, 3) with the .loc accessor on the df DataFrame, it will select the rows starting at index 1 and up to and including index 3.

Example 2: Use slice() With Row Labels

import pandas as pd

df = pd.DataFrame({
    'A': [1, 2, 3, 4],
    'B': [5, 6, 7, 8]
}, index=['w', 'x', 'y', 'z'])

# use slice() to select rows labeled from 'x' to 'y'
result = df.loc[slice('x','y'), :]

print(result)

Output

   A  B
x  2  6
y  3  7

Here, we have first created the df DataFrame with columns A and B. The df is indexed with labels w, x, y, and z.

We used the .loc[] accessor with slice('x', 'y') to select rows. The slice() method here defines the range of the index labels.

The slicing starts with label 'x' and ends with label 'y'(inclusive). The : in the row position indicates that we want to select all rows.

Example 3: Use slice() With Column Labels

import pandas as pd

df = pd.DataFrame({
    'A': [1, 2, 3, 4],
    'B': [5, 6, 7, 8],
    'C': [9, 10, 11, 12],
    'D': [13, 14, 15, 16]
}, index=['w', 'x', 'y', 'z'])

# use slice() to select columns from 'A' to 'C'
result = df.loc[:, slice('A', 'C')]

print(result)

Output

    A   B   C
w  1   5   9
x  2   6  10
y  3   7  11
z  4   8  12

In the above example, we are using slice() within .loc[] to select a range of columns.

Here, slice('A', 'C') means we start from column A and go up to and including column C.

The : in the row position indicates that we want to select all rows.

Example 4: Using slice() With a Specific Step

import pandas as pd

df = pd.DataFrame({
    'A': [1, 2, 3, 4],
    'B': [5, 6, 7, 8],
    'C': [9, 10, 11, 12],
    'D': [13, 14, 15, 16],
    'E': [17, 18, 19, 20]
}, index=['w', 'x', 'y', 'z'])

# select every second column from 'A' to 'E'
result_columns = df.loc[:, slice('A', 'E', 2)]

# select every second row starting from 'w' to 'z'
result_rows = df.loc[slice('w', 'z', 2), :]

print("Selected Columns with Step:\n", result_columns)
print("\nSelected Rows with Step:\n", result_rows)

Output

Selected Columns with Step:
    A   C   E
w  1   9  17
x  2  10  18
y  3  11  19
z  4  12  20

Selected Rows with Step:
    A  B   C   D   E
w  1  5   9  13  17
y  3  7  11  15  19

Here,

• result_columns is obtained by selecting every second column from A to E, using a step of 2. This includes columns A, C, and E.
• result_rows is obtained by selecting every second row from the start of the DataFrame to the end (w to z), also with a step of 2. This includes rows w and y.