How to Sort a Pandas DataFrame by Date?

Sorting a Pandas DataFrame by date is a common operation in data analysis. Pandas provides several methods to accomplish this, with sort_values() being the most efficient. Before sorting, ensure your date column is in proper datetime format using to_datetime().

Basic Date Sorting with sort_values()

The most straightforward method is using sort_values() after converting string dates to datetime format ?

import pandas as pd

# Create sample DataFrame with date strings
data = {
    'Date': ['2023-06-26', '2023-06-24', '2023-06-28', '2023-06-25'], 
    'Sales': [100, 200, 300, 150]
}

df = pd.DataFrame(data)

# Convert Date column to datetime
df['Date'] = pd.to_datetime(df['Date'])

# Sort by date in ascending order
sorted_df = df.sort_values('Date')
print(sorted_df)

        Date  Sales
1 2023-06-24    200
3 2023-06-25    150
0 2023-06-26    100
2 2023-06-28    300

Sorting in Descending Order

To sort dates from newest to oldest, set ascending=False ?

import pandas as pd

data = {
    'Date': ['2023-06-26', '2023-06-24', '2023-06-28', '2023-06-25'], 
    'Sales': [100, 200, 300, 150]
}

df = pd.DataFrame(data)
df['Date'] = pd.to_datetime(df['Date'])

# Sort by date in descending order (newest first)
sorted_df = df.sort_values('Date', ascending=False)
print(sorted_df)

        Date  Sales
2 2023-06-28    300
0 2023-06-26    100
3 2023-06-25    150
1 2023-06-24    200

Using inplace Parameter

Modify the original DataFrame directly using inplace=True instead of creating a new one ?

import pandas as pd

data = {
    'Date': ['2023-06-15', '2023-06-14', '2023-06-17', '2023-06-16'], 
    'Value': [10, 20, 30, 40]
}

df = pd.DataFrame(data)
df['Date'] = pd.to_datetime(df['Date'])

print("Before sorting:")
print(df)

# Sort in-place
df.sort_values('Date', inplace=True)

print("\nAfter sorting:")
print(df)

Before sorting:
        Date  Value
0 2023-06-15     10
1 2023-06-14     20
2 2023-06-17     30
3 2023-06-16     40

After sorting:
        Date  Value
1 2023-06-14     20
0 2023-06-15     10
3 2023-06-16     40
2 2023-06-17     30

Alternative Method Using sorted()

You can also use Python's built-in sorted() function with a lambda key ?

import pandas as pd

data = {
    'Date': ['2023-06-26', '2023-06-24', '2023-06-28'], 
    'Sales': [100, 200, 300]
}

df = pd.DataFrame(data)
df['Date'] = pd.to_datetime(df['Date'])

# Sort using sorted() with lambda
sorted_df = pd.DataFrame(
    sorted(df.values, key=lambda x: x[0]), 
    columns=df.columns
)

print(sorted_df)

        Date  Sales
0 2023-06-24    200
1 2023-06-26    100
2 2023-06-28    300

Comparison of Methods

Conclusion

Use sort_values() for efficient date sorting in Pandas DataFrames. Always convert date strings to datetime format first using to_datetime(). Use inplace=True to modify the original DataFrame and save memory.