How to sort pandas dataframe by one column?

Sorting your DataFrame is straightforward with df.sort_values(). Below is a concise guide on sorting by a single column.

import pandas as pd

df = pd.DataFrame({
    "Name": ["Alice", "Bob", "Charlie", "David"],
    "Age": [25, 30, 35, 40],
    "City": ["New York", "Chicago", "San Francisco", "Seattle"]
})

# Sort DataFrame by the 'Age' column in ascending order
df_sorted_asc = df.sort_values(by="Age")
print(df_sorted_asc)

# Sort DataFrame by the 'Age' column in descending order
df_sorted_desc = df.sort_values(by="Age", ascending=False)
print(df_sorted_desc)

1. sort_values(by="Age") sorts the DataFrame in ascending order based on the values in the "Age" column.
2. ascending=False reverses the sort order, returning the largest values first.
3. The default behavior is to return a new DataFrame with the rows re-ordered. If you want to sort in-place, use df.sort_values(..., inplace=True).

Additional Tips

• Sorting by index: If you need to sort by the DataFrame’s index (row labels), use df.sort_index().
• Sorting by multiple columns: Pass a list to by, for example df.sort_values(by=["Age", "Name"]).
• Handling missing values: By default, NaN values are placed at the end of the sorted list, but you can change this with the na_position parameter (e.g., na_position="first").

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With sort_values(), you can confidently rearrange your DataFrame by any column or set of columns, ensuring your data is properly ordered for analysis, visualization, or exporting.