How to drop the index from Pandas DataFrame? Pandas DataFrames and Pandas Series always have an index, when not specified index while creating, Pandas always creates an index column starting with 0 and incrementing by 1. When you display DataFrmae or Series to the console it displays alongside the column but it is not a column.
Note that in Pandas, technically you can’t drop the index from DataFrame or Series; when you use df.reset_index(drop=True) it actually drops the existing index and resets with the new default index with simple sequential numbers. However, you can drop the index while importing from CSV or exporting DataFrame to a CSV file.
1. Quick Examples of Drop Index from DataFrame & Series
Following are quick examples of how to drop the index column and index level from DataFrame.
# Below are quick examples
# Example 1 - Drop Index and create with new one
df2 = df.reset_index(drop=True)
# Example 2 - Drop Index inplace
df2 = df.reset_index(drop=True, inplace=True)
# Example 3 - Reset the index by setting existing index as column
df2 = df.reset_index()
# Example 4 - Drop index while exporting to CSV
df.to_csv("c:/tmp/courses.csv",index=False)
# Example 5 - Drop index while reading a CSV
df = pd.read_csv("c:/tmp/courses.csv", index_col=False)
# Example 6 - Drop Index level from MultiIndex
df=df.droplevel(0, axis=0)
# Example 7 - Drop index from series
s2 = s2.reset_index(drop=True)
Pandas Index is an immutable sequence used for indexing DataFrame and Series. pandas.Index is a basic object that stores axis labels for all pandas objects.
DataFrame is a two-dimensional data structure, immutable, heterogeneous tabular data structure with labeled axis rows, and columns. pandas Dataframe is consists of three components principal, data, rows, and columns. In DataFrame the row labels are called index.
Let’s create a Pandas DataFrame from Dict.
import pandas as pd
# Create data
technologies = {
'Courses':["Spark","PySpark","Hadoop","Python"],
'Fee' :[20000,25000,26000,22000],
'Duration':['30day','40days','45days', '50days']
}
# Create dataframe
indexes=['r1','r2','r3','r4']
df = pd.DataFrame(technologies,index=indexes)
print(df)
Yields below output.
2. Pandas Drop Index Column from DataFrame
As I said above, technically you can’t drop the index column from the pandas DataFrame however, if you do not want the existing index, you can drop it and re-create it with the default index by using reset_index(). Let’s see it with an example
# Drop Index and create with new one
df2 = df.reset_index(drop=True)
print(df2)
Yields below output.
This by default doesn’t update the existing DataFrame, you can use inplace=True to update the current DataFrame.
# Drop Index inplace
df.reset_index(drop=True, inplace=True)
print(df)
Yields the same output as above.
2.1. Rest Index without Dropping
Here, drop=True is used to completely drop the index from the DataFrame. However, if you wanted to set the index as a column and create a new index do not use this param.
# Reset the index by setting existing index as column
df2 = df.reset_index()
print(df2)
I will leave this to you to run and explore the output.
3. Drop Index Column While Exporting to CSV
By default exporting a pandas DataFrame to CSV includes column names on the first row, row index on the first column, and writes a file with a comma-separated delimiter to separate columns. pandas.DataFrame.to_csv() method provides parameters to ignore an index and header while writing.
To export a DataFrame to CSV by dropping an index in Pandas, use the index=False param. Here, the to_csv() method is used to write Pandas DataFrame to CSV. The below example demonstrates the Pandas drop index.
# Remove index while exporting
df.to_csv("c:/tmp/courses.csv",index=False)
4. Remove Index Column While Importing a CSV
Similarly, you can also drop the index while reading a CSV file into DataFrame.
# Drop Index while reading a CSV file
df = pd.read_csv("c:/tmp/courses.csv", index_col=False)
5. Pandas Drop Index Level from Multi-Index
Use droplevel() to drop an index level from a multi-index dataframe, First, let’s create a Multindex DataFrame in Pandas.
# Create DataFrame with MultIndex
multi_index = pd.MultiIndex.from_tuples([("r0", "rA"),
("r1", "rB")],
names=['index1','index2'])
cols = pd.MultiIndex.from_tuples([("Gasoline", "Toyoto"),
("Gasoline", "Ford"),
("Electric", "Tesla"),
("Electric", "Nio")])
data=[[100,300, 900,400 ], [200,500, 300,600]]
df = pd.DataFrame(data, columns=cols,index=multi_index)
print(df)
#Drop Index from MultiIndex
df=df.droplevel(0, axis=0)
print(df)
Yields below output.
Now, let’s perform Pandas drop index level from MultiIndex by using DataFrame.columns.droplevel() and MultiIndex.droplevel() methods.
Using MultiIndex.droplevel() you can drop single or more levels from multi-level rows/column index. Use axis=1 param to drop columns. To drop row-level use axis=0. The below example drops the first index from DataFrame.
# Drop Index level from MultiIndex
df=df.droplevel(0, axis=0)
print(df)
Yields below output.
6. Drop Index from Series
Like DataFrame, Pandas Series contains an Index that you can’t drop however if you wanted to reset the index starting from zero again, use the following approach.
# Create pandas Series
data = ['python','php','java']
s2 = pd.Series(data, index=['r1', 'r2','r3'])
print(s2)
Now, let’s drop the index
# Drop index from series
s2 = s2.reset_index(drop=True)
print(s2)
7. Conclusion
In this article, you have learned how to perform Pandas drop Index column, index level from DataFrame and Series. As I explained technically you can’t drop the index from DataFrame however you can remove the existing one and create a new index. Also, learned how to drop the index while reading and writing a CSV file.
