You can use reset_index() to create/convert the index/multi-index to a column of pandas DataFrame. Besides this, there are other ways as well. If you are not aware by default, pandas add an index to each row of the pandas DataFrame. This index value starts with zero for the first row and increments by 1 for each row (sequence index value for each row). You can also assign a custom index to DataFrame according to your need.
In this article, I will explain different ways to convert the index to the DataFrame column with examples like adding default index and custom index as a column to DataFrame.
Key Points –
- The primary method to convert the index into a column is using
reset_index(), which moves the index into a regular column. - By default,
reset_index()moves the index to a new column namedindex. - The
reset_index()method can be used with theinplace=Trueparameter to modify the DataFrame without creating a new one. - If the DataFrame has a MultiIndex, all levels of the index will be converted to separate columns when calling
reset_index(). - You can specify specific index levels to reset by passing the level(s) argument to
reset_index()when dealing with MultiIndex DataFrames. - After resetting the index, you may need to rename the new index column for clarity or consistency with other columns.
1. Quick Examples of Convert Index to Column in Pandas DataFrame
Below are some useful examples of how to convert an index to a column in pandas DataFrame.
# Quick examples of convert index to column
# Create a new column with index values
df['index_column'] = df.index
# Using reset_index()
# To convert index into column
df2=df.reset_index()
# Using reset_index, inplace=True
df.reset_index(inplace=True)
# When you have multiple index
# Specify which index you wanted to convert to column
df.reset_index(inplace=True, level=['Fee'])
# Preserve Index as-is and add one index as column
df2 = df.assign(Fee=df.index.get_level_values('Fee'))
# Preserve Index as-is and add one index as column
# Using assign operator
df['Fee'] = df.index.get_level_values('Fee')
Let’s create a DataFrame with a few rows and columns, execute these examples and validate results. Our DataFrame contains column names Courses, Fee, Duration, and Discount.
import pandas as pd
technologies = {
'Courses':["Spark","PySpark","Hadoop","Python","pandas","Oracle","Java"],
'Fee' :[20000,25000,26000,22000,24000,21000,22000],
'Duration':['30day','40days','35days','40days','60days','50days','55days'],
'Discount':[1000,2300,1500,1200,2500,2100,2000]
}
df = pd.DataFrame(technologies)
print(df)
Yields below output.
# Output:
Courses Fee Duration Discount
0 Spark 20000 30day 1000
1 PySpark 25000 40days 2300
2 Hadoop 26000 35days 1500
3 Python 22000 40days 1200
4 pandas 24000 60days 2500
5 Oracle 21000 50days 2100
6 Java 22000 55days 2000
2. Create a New Index Column in DataFrame
One simple way to convert an index to a column is by assigning an index as a new column to DataFrame. DataFrame.index property returns a Series object of an index, assign this to DataFrame as shown below which creates a new column with index value on DataFrame.
# Create a new column with index values
df['index'] = df.index
print(df)
Yields below output. It adds a new column index_column with index values to DataFrame.
# Output:
Courses Fee Duration Discount index_column
0 Spark 20000 30day 1000 0
1 PySpark 25000 40days 2300 1
2 Hadoop 26000 35days 1500 2
3 Python 22000 40days 1200 3
4 pandas 24000 60days 2500 4
5 Oracle 21000 50days 2100 5
6 Java 22000 55days 2000 6
You can also add a new custom column as an index and convert it to a column
# Create DataFrame with custom index
index=['r0','r1','r2','r3','r4','r5','r6']
df = pd.DataFrame(technologies,index=index)
df['index_column'] = df.index
print(df)
Results
# Output:
Courses Fee Duration Discount index_column
r0 Spark 20000 30day 1000 r0
r1 PySpark 25000 40days 2300 r1
r2 Hadoop 26000 35days 1500 r2
r3 Python 22000 40days 1200 r3
r4 pandas 24000 60days 2500 r4
r5 Oracle 21000 50days 2100 r5
r6 Java 22000 55days 2000 r6
3. Convert the Index to Column Using DataFrame.reset_index()
Another way is by using the Dataframe.reset_index() function to convert the index as a column in Pandas DataFrame.
# Using reset_index to convert index to column
df = pd.DataFrame(technologies,index=index)
df2=df.reset_index()
print(df2)
Yields below output. This adds a new column index to DataFrame and returns a copy of the DataFrame instead of updating the existing DataFrame.
# Output:
index Courses Fee Duration Discount
0 r0 Spark 20000 30day 1000
1 r1 PySpark 25000 40days 2300
2 r2 Hadoop 26000 35days 1500
3 r3 Python 22000 40days 1200
4 r4 pandas 24000 60days 2500
5 r5 Oracle 21000 50days 2100
6 r6 Java 22000 55days 2000
Use inplace=True parameter to reflects the change in the DataFrame to stay permanent. If you have multiple indexes, this converts all index levels to columns.
# Convert the index to column
# Using reset_index
df.reset_index(inplace=True)
print(df)
Yields same output as above.
4. Convert the Index to Column Using Rename_Axis and Reset_Index
You can also convert the index to the column using rename_axis() and reset_index() function. You should first rename your index to the custom label and then apply reset_index().
# Using Rename_Axis and Reset_Index
df = pd.DataFrame(technologies,index=index)
df2 = df.rename_axis('index_column').reset_index()
print(df2)
Yields below output.
# Output:
index_column Courses Fee Duration Discount
0 r0 Spark 20000 30day 1000
1 r1 PySpark 25000 40days 2300
2 r2 Hadoop 26000 35days 1500
3 r3 Python 22000 40days 1200
4 r4 pandas 24000 60days 2500
5 r5 Oracle 21000 50days 2100
6 r6 Java 22000 55days 2000
5. Convert Multi_Index to Multiple Columns
Let’s look at another DataFrame with two levels in its index or multi-index.
# Create new DataFrame with multi index
import pandas as pd
new_index = pd.MultiIndex.from_tuples([("Spark", 20000),
("PySpark", 25000),
("Hadoop", 26000),
("Python", 22000),
("pandas", 24000),
("Oracle",21000),
("Java", 23000)],
names=['Courses','Fee'])
Data = {
'Duration':['30day','40days','35days','40days','60days','50days','55days'],
'Discount':[1000,2300,1500,1200,2500,2100,2000],
'Address':["Mumbai","Delhi","Hyderabad","Bangalore","Chennai","Kolkata","Lucknow"]
}
df = pd.DataFrame(Data, columns = ['Duration','Discount','Address'],
index=new_index)
print(df)
Yields below results with two Indexes Courses and Fee
# Output:
Duration Discount Address
Courses Fee
Spark 20000 30day 1000 Mumbai
PySpark 25000 40days 2300 Delhi
Hadoop 26000 35days 1500 Hyderabad
Python 22000 40days 1200 Bangalore
pandas 24000 60days 2500 Chennai
Oracle 21000 50days 2100 Kolkata
Java 23000 55days 2000 Lucknow
Let’s convert these multi-indexes as columns to DataFrame. The reset_index() method with the default parameters converts all index levels to columns and uses a simple RangeIndex as a new index.
# Convert all multi indexes to columns
df.reset_index(inplace=True)
Yields below output. As you see it converted indexes Courses and Fee as DataFrame columns.
# Output:
Courses Fee Duration Discount Address
0 Spark 20000 30day 1000 Mumbai
1 PySpark 25000 40days 2300 Delhi
2 Hadoop 26000 35days 1500 Hyderabad
3 Python 22000 40days 1200 Bangalore
4 pandas 24000 60days 2500 Chennai
5 Oracle 21000 50days 2100 Kolkata
6 Java 23000 55days 2000 Lucknow
6. Using Multiple Columns to Convert Multi_Index and Level
Use the level parameter to control which index to convert when you have multiple indexes on DataFrame. If possible, use the level name, which is more explicit.
# Using multiple columns
# To convert multi_index and level
df = pd.DataFrame(data,index=new_index)
df.reset_index(inplace=True, level=['Fee'])
print(df)
Yields below output
# Output:
Fee Duration Discount Address
Courses
Spark 20000 30day 1000 Mumbai
PySpark 25000 40days 2300 Delhi
Hadoop 26000 35days 1500 Hyderabad
Python 22000 40days 1200 Bangalore
pandas 24000 60days 2500 Chennai
Oracle 21000 50days 2100 Kolkata
Java 23000 55days 2000 Lucknow
In case if you wanted to persist the index as-is and add one of the indexes as a column
df = pd.DataFrame(data,index=new_index)
df2=df.assign(Fee=df.index.get_level_values('Fee'))
print(df2)
# Also use
df['Fee'] = df.index.get_level_values('Fee')
print(df)
Yields below output.
# Output:
Duration Discount Address Fee
Courses Fee
Spark 20000 30day 1000 Mumbai 20000
PySpark 25000 40days 2300 Delhi 25000
Hadoop 26000 35days 1500 Hyderabad 26000
Python 22000 40days 1200 Bangalore 22000
pandas 24000 60days 2500 Chennai 24000
Oracle 21000 50days 2100 Kolkata 21000
Java 23000 55days 2000 Lucknow 23000
Compelte Example
import pandas as pd
technologies = {
'Courses':["Spark","PySpark","Hadoop","Python","pandas","Oracle","Java"],
'Fee' :[20000,25000,26000,22000,24000,21000,22000],
'Duration':['30day','40days','35days','40days','60days','50days','55days'],
'Discount':[1000,2300,1500,1200,2500,2100,2000]
}
df = pd.DataFrame(technologies)
print(df)
# Create a new index
df['index_column'] = df.index
print(df)
index=['r0','r1','r2','r3','r4','r5','r6']
df = pd.DataFrame(technologies,index=index)
df['index_column'] = df.index
print(df)
# Using reset_index
df = pd.DataFrame(technologies,index=index)
df2=df.reset_index()
print(df2)
# Using reset_index, inplace=True
df = pd.DataFrame(technologies,index=index)
df.reset_index(inplace=True)
print(df)
# Using rename_axis() and reset_index()
df = pd.DataFrame(technologies,index=index)
df = df.rename_axis('index_column').reset_index()
print(df)
# Create new DataFrame with multi index
import pandas as pd
new_index = pd.MultiIndex.from_tuples([("Spark", 20000),
("PySpark", 25000),
("Hadoop", 26000),
("Python", 22000),
("pandas", 24000),
("Oracle",21000),
("Java", 23000)],
names=['Courses','Fee'])
data = {
'Duration':['30day','40days','35days','40days','60days','50days','55days'],
'Discount':[1000,2300,1500,1200,2500,2100,2000],
'Address':["Mumbai","Delhi","Hyderabad","Bangalore","Chennai","Kolkata","Lucknow"]
}
df = pd.DataFrame(data, columns = ['Duration','Discount','Address'],
index=new_index)
print(df)
# convert multi_index
df.reset_index(inplace=True)
print(df)
# Using multiple columns
# To convert multi_index and level
df = pd.DataFrame(data,index=new_index)
df.reset_index(inplace=True, level=['Fee'])
print(df)
FAQ on Convert Index to Column in Pandas DataFrame
To convert the index to a column in a Pandas DataFrame, you can use the reset_index() function. This function moves the current index to a column and replaces it with a default integer index.
reset_index() moves the index of a DataFrame into a column and creates a new default integer-based index. If inplace=True, it modifies the DataFrame directly. Otherwise, it returns a new DataFrame.
When using reset_index(), it will convert the index to a column without affecting other columns. You can also specify which level of a MultiIndex to reset by passing the level parameter.
You can rename the index column after using reset_index() in Pandas. By default, when you reset the index, the column that was once the index is named “index”. You can rename it using the rename() method.
If your DataFrame has a MultiIndex (multiple index levels), you can use reset_index() to convert one or more index levels into columns. Use the level argument to specify which level(s) to reset
Conclusion
In this article, you have learned how to convert an index to a column in pandas DataFrame using index(), rename_axis() and reset_index() function. Also, you have learned how to convert multiple indexes into columns use these methods.
Happy Learning !!
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