How to Insert a Row into a Pandas Dataframe using loc, Shifting Index, and Sorting

Inserting a Row into a Pandas Dataframe

When working with Pandas dataframes, you may encounter the need to insert a new row at a specific location. Suppose you have a dataframe with two series, s1 and s2, represented as follows:

<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)</code>

   A  B  C
0  5  6  7
1  7  8  9

[2 rows x 3 columns]

To add a new row with values [2, 3, 4] as the first row, follow these steps:

1. Assign Row to a Specific Index using loc:

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

2. Shift Index by 1:

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

3. Sort by Index:

<code class="python">df = df.sort_index()  # sorting by index</code>

After performing these steps, you will obtain the desired output:

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

As explained in the Pandas documentation on Indexing: Setting with enlargement, this approach allows you to add new rows to a dataframe by enlarging the index. The loc function lets you assign values to a specific index, in this case, -1 for the new row. Shifting the index and sorting by index ensures that the new row is inserted as the first row in the dataframe.

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