How Can I Efficiently Append Rows to a Pandas DataFrame One at a Time?

Appending Rows One at a Time to a Pandas Dataframe

Creating a blank dataframe and subsequently appending rows one by one is a fundamental task for data manipulation in Pandas.

Initial Approach: Incremental Field Updates

One method for appending a single row is via incremental updates. For instance, if we have an empty dataframe with columns 'lib', 'qty1', and 'qty2', we can add a row by individually setting the values for each column:

df = pd.DataFrame(columns=('lib', 'qty1', 'qty2'))
df = df._set_value(index=len(df), col='qty1', value=10.0)

While this approach allows for selective field updates, it becomes cumbersome for bulk insertions.

Optimized Approach: Row-Based Appending

A more efficient and comprehensive method for appending rows is through row-based assignment. This involves using df.loc[i] to specify a specific row index. Here's how to implement it:

import pandas as pd
from numpy.random import randint

df = pd.DataFrame(columns=['lib', 'qty1', 'qty2'])
for i in range(5):
    df.loc[i] = ['name' + str(i)] + list(randint(10, size=2))

df

In this example, the df.loc[i] syntax assigns a row at index i with a list comprising a string and two random integers.

This approach provides a concise and efficient way to append multiple rows to a dataframe, significantly reducing the code complexity and improving efficiency.

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