How to Efficiently Append Multiple Pandas DataFrames at Once?

Efficiently Append Multiple Pandas Data Frames Simultaneously

Merging multiple data frames is a common task in data analysis. However, appending them one by one can be tedious and time-consuming. Fortunately, Pandas provides an efficient way to append multiple data frames in a single operation.

Let's consider the following situation: you have five data frames named t1, t2, t3, t4, and t5. To append them at once, you can utilize the pd.concat() function.

<code class="python">import pandas as pd

df = pd.concat([t1, t2, t3, t4, t5])</code>

By default, pd.concat() will stack the data frames vertically, creating a single, cohesive data frame. You can also specify the axis parameter to append the data frames horizontally.

<code class="python">df = pd.concat([t1, t2, t3, t4, t5], axis=1)</code>

To avoid duplicate index values, use the ignore_index parameter:

<code class="python">df = pd.concat([t1, t2, t3, t4, t5], ignore_index=True)</code>

This will create a new index for the combined data frame. Note that if the data frames have different column names, the resulting data frame will contain the union of all column names.

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