How to Insert a Row into a Pandas DataFrame at a Specified Position?

Inserting a Row into a Pandas DataFrame

In Pandas, you have a DataFrame and want to insert a row at a specific index or position. A common way is using loc (for label-based indexing) or iloc (for integer-based indexing).

Let's understand this with an example:

Consider the following DataFrame df:

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)

Output:

   A  B  C
0  5  6  7
1  7  8  9

Now, you want to insert a new row (e.g., [2, 3, 4]) to the beginning (index 0) of the existing DataFrame.

To insert a row at a specific index, you can assign the row to the desired index using loc. For instance, to insert [2, 3, 4] as the first row:

df.loc[-1] = [2, 3, 4]

However, this action adds a row before the existing DataFrame, resulting in a negative index. To shift the existing indices up and make the newly inserted row the first row, you can update the indices by incrementing them:

df.index = df.index + 1

Finally, sort the DataFrame by the index to obtain the desired ordering:

df = df.sort_index()

As a result, you get the updated DataFrame with the inserted row at index 0:

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

This approach allows you to insert a new row at any position within a DataFrame by assigning values to a specific index.

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