Pandas Concatenation: Where Dataframes Come Together

Pandas provide several functions for concatenating DataFrames, including pd.concat(), pd.append(), and pd.merge().

The pd.concat() function concatenates DataFrames along a particular axis. By default, the axis is axis=0, which means that the DataFrames are concatenated vertically (i.e., row-wise). You can specify axis=1 to concatenate horizontally (i.e., column-wise).

Here’s an example of how to use pd.concat() to concatenate two DataFrames vertically:

import pandas as pd

df1 = pd.DataFrame({'A': ['A0', 'A1', 'A2'],
                    'B': ['B0', 'B1', 'B2'],
                    'C': ['C0', 'C1', 'C2'],
                    'D': ['D0', 'D1', 'D2']},
                   index=[0, 1, 2])
df2 = pd.DataFrame({'A': ['A3', 'A4', 'A5'],
                    'B': ['B3', 'B4', 'B5'],
                    'C': ['C3', 'C4', 'C5'],
                    'D': ['D3', 'D4', 'D5']},
                   index=[3, 4, 5])
df3 = pd.concat([df1, df2])

print(df3)

This will output the following DataFrame:

   A   B   C   D
0  A0  B0  C0  D0
1  A1  B1  C1  D1
2  A2  B2  C2  D2
3  A3  B3  C3  D3
4  A4  B4  C4  D4
5  A5  B5  C5  D5

The pd.append() function is a convenient shortcut for concatenating DataFrames vertically. It works the same as pd.concat(), but with a default axis=0 and the ability to specify the DataFrames to be concatenated as separate arguments instead of a list.

Here’s an example of how to use pd.append() to concatenate two DataFrames:

df4 = pd.DataFrame({'A': ['A6', 'A7', 'A8'],
                    'B': ['B6', 'B7', 'B8'],
                    'C': ['C6', 'C7', 'C8'],
                    'D': ['D6', 'D7', 'D8']},
                   index=[6, 7, 8])
df5 = pd.DataFrame({'A': ['A9', 'A10', 'A11'],
                    'B': ['B9', 'B10', 'B11'],
                    'C': ['C9', 'C10', 'C11'],
                    'D': ['D9', 'D10', 'D11']},
                   index=[9, 10, 11])
df6 = df3.append(df4, ignore_index=True).append(df5, ignore_index=True)

Pandas provides several ways to concatenate DataFrames and Series, which allows you to combine data from multiple sources into a single DataFrame or Series. Here are some of examples of how to use concatenation in Pandas:

pd.concat(): This method is used to concatenate multiple DataFrames or Series along a specific axis (rows or columns).

# Concatenate two DataFrames along rows
pd.concat([df1, df2])

# Concatenate two DataFrames along columns
pd.concat([df1, df2], axis=1)

pd.append(): This method is used to append rows to a DataFrame. It is a shorthand for pd.concat([df, new_row])

# Append a new row to a DataFrame
df.append(new_row)

pd.merge(): This method is used to merge two DataFrames on a specific column or index. It is similar to a SQL join operation.

# Merge two DataFrames on a specific column
pd.merge(df1, df2, on='Name')

pd.join(): This method is used to join two DataFrames on a specific column or index. It is similar to a SQL join operation.

# Join two DataFrames on a specific column
df1.join(df2, on='Name')

These are some examples of how to use concatenation in Pandas. The concatenation functions in Pandas provide a powerful and flexible way to combine data from multiple sources into a single DataFrame or Series, which can be useful for data cleaning, data wrangling, data analysis, and data modelling.