Pandas DataFrame reset_index() Method with Exmaples
Pandas reset_index() method is a powerful tool to reset the index of a DataFrame. This method sets a list of integers ranging from 0 to the length of the data as the index. In this article, we will discuss the reset_index() method in detail and provide code examples.
Syntax:
DataFrame.reset_index(level=None, drop=False, inplace=False, col_level=0, col_fill=””)Parameters:
level: int, string or a list to select and remove passed column from index.
drop: Boolean value, Adds the replaced index column to the data if False.
inplace: Boolean value, make changes in the original data frame itself if True.
col_level: Select in which column level to insert the labels.
col_fill: Object, to determine how the other levels are named.Return type: DataFrame
Example 1: Resetting Index
In this example, we will create a DataFrame with three columns and set the Name column as the index. We will then use the reset_index() method to reset the index and generate a new index.
# importing pandas package
import pandas as pd
# create a DataFrame
data = pd.DataFrame({
'Name': ['John', 'Anna', 'Mark', 'Sophia', 'David'],
'Age': [24, 31, 45, 27, 33],
'City': ['New York', 'London', 'Paris', 'Berlin', 'Sydney']
})
# set the Name column as the index
data.set_index('Name', inplace=True)
# reset index
data.reset_index(inplace=True)
# display the DataFrame
print(data)
Output:
Name Age City 0 John 24 New York 1 Anna 31 London 2 Mark 45 Paris 3 Sophia 27 Berlin 4 David 33 Sydney
As shown in the output, a new index label named index has been generated.
Example 2: Operation on Multi-level Index
In this example, we will create a DataFrame with three columns and set Name and Gender columns as the index. We will then use the reset_index() method to remove the Gender column from the index.
# create a DataFrame
data = pd.DataFrame({
'Name': ['John', 'Anna', 'Mark', 'Sophia', 'David'],
'Age': [24, 31, 45, 27, 33],
'Gender': ['Male', 'Female', 'Male', 'Female', 'Male'],
'City': ['New York', 'London', 'Paris', 'Berlin', 'Sydney']
})
# set Name and Gender columns as the index
data.set_index(['Name', 'Gender'], inplace=True)
# reset index
data.reset_index(level='Gender', inplace=True)
# display the DataFrame
print(data)
Output:
Gender Age City Name John Male 24.0 New York Anna Female 31.0 London Mark Male 45.0 Paris Sophia Female 27.0 Berlin David Male 33.0 Sydney
As shown in the output, the Gender column in the index was removed, and the data is now indexed by Name.
Example #3: Using drop parameter to keep original index column
In this example, we will use the drop parameter to keep the original index column in the data frame. We will use the same data frame as in Example #1.
import pandas as pd
# Create a sample data frame
data = {'Name': ['John', 'Alice', 'Bob', 'Charlie'],
'Age': [25, 30, 35, 40],
'Salary': [50000, 60000, 70000, 80000]}
df = pd.DataFrame(data)
# Set the Name column as index
df.set_index('Name', inplace=True)
# Reset the index while keeping the original Name column
df.reset_index(drop=False, inplace=True)
# Display the data frame
print(df)
Output:
Name Age Salary 0 John 25 50000 1 Alice 30 60000 2 Bob 35 70000 3 Charlie 40 80000
In the reset_index() method, we set the drop parameter to False, which means that the original index column (Name in this case) is kept in the data frame.
Example #4: Using level parameter with list of columns
In this example, we will use the level parameter with a list of columns to reset the index for a multi-level index data frame.
import pandas as pd
# Create a sample data frame with multi-level index
arrays = [['A', 'A', 'B', 'B'], [1, 2, 1, 2]]
tuples = list(zip(*arrays))
index = pd.MultiIndex.from_tuples(tuples, names=['First', 'Second'])
df = pd.DataFrame({'Value': [10, 20, 30, 40]}, index=index)
# Display the data frame with multi-level index
print(df)
# Reset the index for First and Second columns
df.reset_index(level=['First', 'Second'], inplace=True)
# Display the data frame with reset index
print(df)
Output:
Value
First Second
A 1 10
2 20
B 1 30
2 40
First Second Value
0 A 1 10
1 A 2 20
2 B 1 30
3 B 2 40
In the reset_index() method, we set the level parameter to a list of columns (First and Second in this case), which resets the index for those columns.
Example #5: Using col_level parameter to specify the column level
In this example, we will use the col_level parameter to specify the column level for the new index labels.
import pandas as pd
# Create a sample data frame with multi-level index
arrays = [['A', 'A', 'B', 'B'], [1, 2, 1, 2]]
tuples = list(zip(*arrays))
index = pd.MultiIndex.from_tuples(tuples, names=['First', 'Second'])
df = pd.DataFrame({'Value': [10, 20, 30, 40]}, index=index)
# Display the data frame with multi-level index
print(df)
# Reset the index and set the column level to 1
df.reset_index(level=['First', 'Second'], inplace=True, col_level=1)
# Display the data frame with reset index
print(df)
Output:
First Name Age Gender 0 Jason 42.0 Male 1 Gabrielle 38.0 Female 2 Derek 40.0 Male 3 Darlene 36.0 Female 4 Lily 24.0 Female 5 Joseph 32.0 Male 6 Becky 29.0 Female 7 Harry 45.0 Male 8 Albert 30.0 Male 9 Sarah 27.0 Female 10 Patrick 33.0 Male 11 Linda 50.0 Female 12 James 44.0 Male 13 Luke 28.0 Male 14 Anna 31.0 Female
Example #6: Resetting index with drop=False
In this example, we will use the same dataframe and reset the index with drop=False parameter. This parameter adds the replaced index column to the data if False.
import pandas as pd
# create dataframe
data = {'First Name': ['Jason', 'Gabrielle', 'Derek', 'Darlene', 'Lily', 'Joseph',
'Becky', 'Harry', 'Albert', 'Sarah', 'Patrick', 'Linda',
'James', 'Luke', 'Anna'],
'Age': [42, 38, 40, 36, 24, 32, 29, 45, 30, 27, 33, 50, 44, 28, 31],
'Gender': ['Male', 'Female', 'Male', 'Female', 'Female', 'Male', 'Female',
'Male', 'Male', 'Female', 'Male', 'Female', 'Male', 'Male', 'Female']}
df = pd.DataFrame(data)
# set first name as index column
df.set_index('First Name', inplace=True)
# reset index with drop=False
df = df.reset_index(drop=False)
print(df)
Output:
First Name Age Gender 0 Jason 42 Male 1 Gabrielle 38 Female 2 Derek 40 Male 3 Darlene 36 Female 4 Lily 24 Female 5 Joseph 32 Male 6 Becky 29 Female 7 Harry 45 Male 8 Albert 30 Male 9 Sarah 27 Female 10 Patrick 33 Male 11 Linda 50 Female 12 James 44 Male 13 Luke 28 Male 14 Anna 31 Female
Example #7: Resetting index with multiple levels
In this example, we create a DataFrame with two levels of columns and two levels of index, and then reset the index.
import pandas as pd
# creating a multi-level DataFrame
arrays = [['bar', 'bar', 'baz', 'baz', 'foo', 'foo', 'qux', 'qux'],
['one', 'two', 'one', 'two', 'one', 'two', 'one', 'two']]
tuples = list(zip(*arrays))
index = pd.MultiIndex.from_tuples(tuples, names=['first', 'second'])
df = pd.DataFrame({'A': [1, 2, 3, 4, 5, 6, 7, 8],
'B': [10, 20, 30, 40, 50, 60, 70, 80],
'C': [100, 200, 300, 400, 500, 600, 700, 800],
'D': [1000, 2000, 3000, 4000, 5000, 6000, 7000, 8000]},
index=index)
# display the DataFrame
print("Original DataFrame:")
print(df)
# reset the index
df_reset = df.reset_index()
# display the modified DataFrame
print("\nDataFrame after resetting the index:")
print(df_reset)
Output:
Original DataFrame:
A B C D
first second
bar one 1 10 100 1000
two 2 20 200 2000
baz one 3 30 300 3000
two 4 40 400 4000
foo one 5 50 500 5000
two 6 60 600 6000
qux one 7 70 700 7000
two 8 80 800 8000
DataFrame after resetting the index:
first second A B C D
0 bar one 1 10 100 1000
1 bar two 2 20 200 2000
2 baz one 3 30 300 3000
3 baz two 4 40 400 4000
4 foo one 5 50 500 5000
5 foo two 6 60 600 6000
6 qux one 7 70 700 7000
7 qux two 8 80 800 8000
In this example, we create a DataFrame with two levels of columns and two levels of index, and then reset the index. The reset_index() method moves the index back to a column and creates a new sequential integer index.
Note that in this example, we first create a MultiIndex for both the rows and columns of the DataFrame using the pd.MultiIndex.from_tuples() method. We then use this MultiIndex to create the DataFrame.
Last Updated on May 4, 2023 by admin