How to Insert Rows into Pandas Dataframes: A Guide Through the Steps

Inserting Rows into Pandas Dataframes: A Step-by-Step Guide

Introduction

Data manipulation is a crucial aspect of data analysis, and managing dataframes is a core part of this process. One common task involves adding or inserting rows into dataframes to expand the dataset. This article provides a comprehensive guide to inserting rows into Pandas dataframes.

Background

Consider the following dataframe:

<code class="python">s1 = pd.Series([5, 6, 7])
s2 = pd.Series([7, 8, 9])

df = pd.DataFrame([list(s1), list(s2)], columns=["A", "B", "C"])

print(df)

   A  B  C
0  5  6  7
1  7  8  9</code>

The objective is to insert a new row [2, 3, 4] into this dataframe, resulting in the following output:

A B C
0 2 3 4
1 5 6 7
2 7 8 9

Solution

Step 1: Assign the New Row

The first step is to assign the new row to a specific index in the dataframe. Pandas provides the loc accessor to access a specific row or column by index. To insert the new row at the beginning of the dataframe, you can use the negative index -1 as follows:

<code class="python">df.loc[-1] = [2, 3, 4]</code>

Step 2: Shift the Index

After assigning the new row, the dataframe's index is not aligned correctly. To fix this, use the index attribute and add an increment to shift the index by one.

<code class="python">df.index = df.index + 1</code>

Step 3: Sort by Index

Finally, to ensure that the rows are sorted by row index, call the sort_index() method.

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

Output

The updated dataframe is as follows:

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

    A  B  C
 0  2  3  4
 1  5  6  7
 2  7  8  9</code>

Conclusion

This step-by-step guide effectively addresses the challenge of inserting rows into Pandas dataframes. Utilizing Pandas' loc accessor, index manipulation, and sorting capabilities, you can seamlessly expand your dataframes and perform robust data analysis operations.

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