How do I reset the index of a Pandas DataFrame after removing rows?

Method to Reset Index in a Pandas Dataframe

Resetting the index of a dataframe can be necessary when you remove rows and want to keep a continuous index. In this case, you may encounter the problem of having an irregular index such as [1, 5, 6, 10, 11]. To remedy this, pandas provides a convenient solution with the DataFrame.reset_index method.

Example:

Consider the following dataframe with an irregular index:

<code class="python">import pandas as pd

df = pd.DataFrame({'a': [1, 3, 5, 7, 9], 'b': [2, 4, 6, 8, 10]}, index=[1, 5, 6, 10, 11])</code>

Solution:

To reset the index, use the reset_index method:

<code class="python">df = df.reset_index()</code>

This will create a new column named 'index' with the original index values. To remove this column, use the drop parameter:

<code class="python">df = df.reset_index(drop=True)</code>

Now, the dataframe will have a continuous index starting from 0:

<code class="python">print(df)

   a  b
0  1  2
1  3  4
2  5  6
3  7  8
4  9 10</code>

Alternative Method:

Instead of reassigning the dataframe, you can use the inplace parameter to modify it directly:

<code class="python">df.reset_index(drop=True, inplace=True)</code>

Note: Using the reindex method will not reset the index of the dataframe.

The above is the detailed content of How do I reset the index of a Pandas DataFrame after removing rows?. For more information, please follow other related articles on the PHP Chinese website!