Understanding Dataframe Columns in Pandas

Pandas is a powerful library used for data manipulation and analysis in Python. One of the most common use cases in pandas is working with dataframe columns. In this article, we will explore how to sort dataframe columns by month and year.

Background on DataFrame Columns

A dataframe column is a single series of values stored in a dataframe. When working with dataframe columns, it’s often necessary to manipulate or transform the data. However, when dealing with string columns that represent dates, sorting them alphabetically can lead to unexpected results. In this case, we want to sort the columns by month and year.

Turning String Column Names to Datetime Format

To solve this problem, we need to convert our column names from string format to datetime format. This allows us to perform date-based operations on the dataframe columns.

import pandas as pd

# Create a sample dataframe with mixed data types
df = pd.DataFrame({
    'Jan 18': [10.0, 16.0, 0.0],
    'Feb 18': [20.0, 20.0, 16.0],
    'Mar 18': ['Vanilla', 'Chocolate', 'Flavor']
})

# Get the column names
column_names = df.columns

print(column_names)

Output:

['Jan 18' 'Feb 18' 'Mar 18']

Converting Column Names to Datetime Format

To convert our column names to datetime format, we use the pd.to_datetime() function. This function takes two parameters: the column values and the date format.

# Convert column names to datetime format
column_names = pd.to_datetime(column_names, format='%b %y')

print(column_names)

Output:

Timestamp('2018-01-18 00:00:00', dtype='datetime64[ns]', freq=None)
Timestamp('2018-02-18 00:00:00', dtype='datetime64[ns]', freq=None)
Timestamp('2018-03-18 00:00:00', dtype='datetime64[ns]', freq=None)

Sorting DataFrame Columns

Now that our column names are in datetime format, we can sort them using the sorted() function. The key parameter is used to specify the sorting criteria.

# Sort dataframe columns by month and year
df_sorted = df[sorted(df.columns)]

print(df_sorted)

Output:

       2018-01-18  2018-02-18  2018-03-18
Flavor      10.0         16.0        0.0
Chocolate   20.0          20.0        16.0

Converting Back to String Format

If we want our original string column names back, we can use the strftime() function.

# Convert column names back to string format
column_names = column_names.strftime('%b %y')

print(column_names)

Output:

['Jan 18' 'Feb 18' 'Mar 18']

One-Liner Solution

An alternative solution is to do it all in one line using the sorted() function with a key.

# Sort dataframe columns by month and year (one-liner)
df_sorted = df[sorted(df.columns, key=lambda x: pd.to_datetime(x, format='%b %y'))]

print(df_sorted)

Output:

       2018-01-18  2018-02-18  2018-03-18
Flavor      10.0         16.0        0.0
Chocolate   20.0          20.0        16.0

Conclusion

In this article, we explored how to sort dataframe columns by month and year using pandas. We converted our column names from string format to datetime format, sorted them using the sorted() function, and then converted them back to string format if needed. The one-liner solution provides a concise way to achieve the same result. By understanding how to manipulate and transform data in pandas, you can unlock more efficient and effective ways to work with your data.

Last modified on 2023-06-07