In this article, we will learn how to slice a DataFrame column-wise in Python. DataFrame is a two-dimensional tabular data structure with labeled axes. i.e. columns.
Creating Dataframe to slice columns
Python3
# importing pandasimport pandas as pd# Using DataFrame() method from pandas moduledf1 = pd.DataFrame({"a": [1, 2, 3, 4, 5, 6, 7], "b": [2, 3, 4, 2, 3, 4, 5], "c": [3, 4, 5, 2, 3, 4, 5], "d": [4, 5, 6, 2, 3, 4, 5], "e": [5, 6, 7, 2, 3, 4, 5]})display(df1) |
Output:
Method 1: Slice Columns in pandas using reindex
Slicing column from ‘c’ to ‘b’.
Python3
df2 = df1.reindex(columns = ['c','b'])print(df2) |
Output:
Method 2: Slice Columns in pandas using loc[]
The df.loc[] is present in the Pandas package loc can be used to slice a Dataframe using indexing. Pandas DataFrame.loc attribute accesses a group of rows and columns by label(s) or a boolean array in the given DataFrame.
Syntax: [ : , first : last : step]
Example 1:
Slicing column from ‘b’ to ‘d’ with step 2.
Python3
df2 = df1.loc[:, "b":"d":2]print(df2) |
Output:
Example 2:
Slicing column from ‘c’ to ‘e’ with step 1.
Python3
df2 = df1.loc[:, "c":"e":1]print(df2) |
Output:
Method 3: Slice Columns in pandas using iloc[]
The iloc is present in the Pandas package. The iloc can be used to slice a Dataframe using indexing. df.iloc[] method is used when the index label of a data frame is something other than numeric series of 0, 1, 2, 3….n or in case the user doesn’t know the index label. Rows can be extracted using an imaginary index position that isn’t visible in the data frame.
Syntax: [ start : stop : step]
Example 1:
Slicing column from ‘1’ to ‘3’ with step 1.
Python3
df2 = df1.iloc[:, 1:3:1]print(df2) |
Output:
Example 2:
Slicing column from ‘0’ to ‘3’ with step 2.
Python3
df2 = df1.iloc[:, 0:3:2]print(df2) |
Output:
