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Reindexing in Pandas can be used to change the index of rows and columns in a DataFrame. Indexes can be used with reference to multiple DataStructure indexes associated with multiple panda series or panda DataFrame. Let’s see how we can reindex columns and rows in a Pandas DataFrame.
Reindex Rows
You can reindex one or more rows using reindex ()
. The default values ​​in the new index that are not in the data frame are assigned NaN.
Example # 1:
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Output:
Example # 2:
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Output:
Re-indexing columns using keyword axis
It is possible to reindex one column or multiple columns using reindex ()
and specifying the axis
we want to reindex. The default values ​​in the new index that are not in the data frame are assigned NaN.
Example # 1:
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Output:
Example # 2:
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Output:
Replacing missing values ​​
Code # 1: You can fill in missing values ​​from a data frame by passing a value to the keyword fill_value
. This keyword replaces NaN values.
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Output:
Code # 2: Replace missing data with a string.
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Output:
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We hope this article has helped you to resolve the problem. Apart from Reindexing in Pandas DataFrame, check other array Python module-related topics.
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San Francisco | 2023-02-01
Thanks for explaining! I was stuck with Reindexing in Pandas DataFrame for some hours, finally got it done 🤗. I am just not quite sure it is the best method
Milan | 2023-02-01
Thanks for explaining! I was stuck with Reindexing in Pandas DataFrame for some hours, finally got it done 🤗. Checked yesterday, it works!
Prague | 2023-02-01
string Python module is always a bit confusing 😭 Reindexing in Pandas DataFrame is not the only problem I encountered. I am just not quite sure it is the best method