Concatenation of Data Objects

Concatenation stacks two or more DataFrames together - either on top of each other (adding more rows) or side by side (adding more columns). Unlike merging, it doesn't require a shared key column.

Stacking Rows (Vertical Concatenation)

import pandas as pd

batch1 = pd.DataFrame({"Name": ["Abhay", "Priya"], "Score": [85, 92]})
batch2 = pd.DataFrame({"Name": ["Rohit", "Simran"], "Score": [78, 88]})

combined = pd.concat([batch1, batch2], ignore_index=True)
print(combined)
#      Name  Score
# 0   Abhay     85
# 1   Priya     92
# 2   Rohit     78
# 3  Simran     88

Stacking Columns (Horizontal Concatenation)

contact_info = pd.DataFrame({"Phone": ["9876543210", "9876543211"]})
result = pd.concat([batch1, contact_info], axis=1)

Handling Mismatched Columns

# If column names don't match exactly, missing values become NaN
df_a = pd.DataFrame({"Name": ["Abhay"], "Score": [85]})
df_b = pd.DataFrame({"Name": ["Priya"], "Grade": ["A"]})

result = pd.concat([df_a, df_b], ignore_index=True)
print(result)
#     Name  Score Grade
# 0  Abhay   85.0   NaN
# 1  Priya    NaN     A

Coming Up Next

Next, you'll dig deeper into the different types of joins available while merging data objects.