As a data analyst or a data scientist, you might have come across the need to iterate over a Pandas dataframe rows. In this article, we will discuss how to iterate over rows of a Pandas dataframe and some best practices for doing so.
Loading a Sample Pandas Dataframe
Copy the code below if you’d like to follow along with an example dataframe. To print a small dataframe in its totality, we will load it. Running this dataframe should not cause any significant speed issues, but they will become more apparent as your dataset expands.
import pandas as pd
# create a sample dataframe
data = {'name': ['John', 'Emily', 'Kate', 'Sam'],
'age': [25, 30, 28, 35],
'gender': ['Male', 'Female', 'Female', 'Male']}
df = pd.DataFrame(data)
# rename the index using the rename() method
df = df.rename(index={0: 'row1', 1: 'row2', 2: 'row3', 3: 'row4'})
# print the updated dataframe
print(df)
Output:
# name age gender
# 0 John 25 Male
# 1 Emily 30 Female
# 2 Kate 28 Female
# 3 Sam 35 Male
This code will create a dataframe with three columns (‘name’, ‘age’, and ‘gender’) and four rows of data. We then create a Pandas DataFrame from the dictionary using the pd.DataFrame() constructor. Finally, we print the DataFrame to verify that it was created correctly.
How to Rename a Pandas Dataframe Index
To do that, you can rename the index of a Pandas dataframe using the rename() method or by directly assigning a new name to the index.name attribute.
Here’s an example:
import pandas as pd
# create a sample dataframe
data = {'name': ['John', 'Emily', 'Kate', 'Sam'],
'age': [25, 30, 28, 35],
'gender': ['Male', 'Female', 'Female', 'Male']}
df = pd.DataFrame(data)
# rename the index using the rename() method
df = df.rename(index={0: 'row1', 1: 'row2', 2: 'row3', 3: 'row4'})
# print the updated dataframe
print(df)
Output:
# name age gender
# row1 John 25 Male
# row2 Emily 30 Female
# row3 Kate 28 Female
# row4 Sam 35 Male
In this method, we use the rename() method to rename the index of the dataframe. The index parameter takes a dictionary where the keys are the old index values, and the values are the new index values. In this example, we are renaming the index values 0, 1, 2, and 3 to row1, row2, row3, and row4, respectively.
Second method: Assigning a new name to index.name attribute.
Here’s an example:
import pandas as pd
# create a sample dataframe
data = {'name': ['John', 'Emily', 'Kate', 'Sam'],
'age': [25, 30, 28, 35],
'gender': ['Male', 'Female', 'Female', 'Male']}
df = pd.DataFrame(data)
# rename the index using the index.name attribute
df.index.name = 'row_id'
# print the updated dataframe
print(df)
Output:
# name age gender
# row_id
# 0 John 25 Male
# 1 Emily 30 Female
# 2 Kate 28 Female
# 3 Sam 35 Male
In this method, we directly assign a new name to the index.name attribute. In this example, we are renaming the index name to row_id.
Both methods will produce the same result, which is a dataframe with the updated index names.
How to Rename a Pandas Multi-Index
It can be challenging to work with Pandas multi-index dataframes, but changing their indices doesn’t have to be. You can rename a Pandas multi-index using the rename() method.
Here’s an example:
import pandas as pd
# create a sample dataframe with multi-index
data = {'name': ['John', 'John', 'Emily', 'Emily', 'Kate', 'Kate', 'Sam', 'Sam'],
'gender': ['Male', 'Male', 'Female', 'Female', 'Female', 'Female', 'Male', 'Male'],
'age': [25, 26, 30, 31, 28, 29, 35, 36]}
multi_index = pd.MultiIndex.from_arrays([['A', 'A', 'B', 'B', 'C', 'C', 'D', 'D'], ['M', 'F', 'M', 'F', 'F', 'M', 'M', 'F']], names=['Group', 'Gender'])
df = pd.DataFrame(data, index=multi_index)
# rename the multi-index using the rename() method
df = df.rename(index={'A': 'Group1', 'B': 'Group2', 'C': 'Group3', 'D': 'Group4'})
# print the updated dataframe
print(df)
Output:
# name gender #age
# Group Gender
# Group1 M John Male 25
# F John Male 26
# Group2 M Emily Female 30
#F Emily Female 31
# Group3 F Kate Female 28
# M Kate Female 29
# Group4 M Sam Male 35
# F Sam Male 36
In this example, we first create a sample dataframe df with a multi-index consisting of two levels (‘Group’ and ‘Gender’). We then use the rename() method to rename the levels of the multi-index. The index parameter takes a dictionary where the keys are the old level names, and the values are the new level names. In this case, we are renaming the level names ‘A’, ‘B’, ‘C’, and ‘D’ to ‘Group1’, ‘Group2’, ‘Group3’, and ‘Group4’, respectively.
Finally, we print the updated dataframe using the print() function. Note that this method only changes the names of the levels and does not change the actual index values.
How to Remove a Pandas Index Name
To remove a Pandas dataframe index name, we can simply use the rename_axis() method.
Here’s an example:
import pandas as pd
# create a sample dataframe with an index name
data = {'name': ['John', 'Emily', 'Kate', 'Sam'],
'age': [25, 30, None, 35]}
df = pd.DataFrame(data, index=['row1', 'row2', 'row3', 'row4'])
df.index.name = 'ID'
# remove the index name by setting it to an empty string
df.index.name = None
# print the updated dataframe
print(df)
Output:
# name age
# row1 John 25.0
# row2 Emily 30.0
# row3 Kate NaN
# row4 Sam 35.0
In this example, we first create a sample dataframe df with an index name ‘ID’. We then use the rename_axis() method to remove the name of the index. We pass None to the method, which removes the name of the index.
Finally, we print the updated dataframe using the print() function. Note that after removing the index name, the output will not display the name of the index.
Wrap up
To learn more about the Pandas dataframe index.name attribute, check out the the official documentation here:
https://pandas.pydata.org/docs
Thanks for reading. Happy coding!
