Sort Pandas DataFrame by Date (Datetime)

We can sort Pandas DataFrame by date using sort_values() function. Using this function we can sort single and multiple DateTime columns in ascending, descending, and even a variable order of a given DataFrame. In this article, I will explain the sort_values() function and using this how we can sort the Datetime column by DateTime with examples.

Key Points –

• Use the sort_values() method in Pandas to sort a DataFrame by a DateTime column.
• Specify the DateTime column to sort by using the by parameter.
• Use the ascending parameter to control the sorting order (default is ascending).
• Utilize the inplace parameter to apply sorting directly to the DataFrame.
• Optionally, reset the index after sorting using the reset_index() method.
• Sorting can be performed in place using inplace=True, or it can return a new DataFrame.
• Set ascending=True for oldest to newest and ascending=False for reverse order.
• Multiple columns can be used for sorting by passing a list to by in sort_values().

<a id="h-quick-examples-of-sort-pandas-dataframe-by-date"></a>

Quick Examples of Sort Pandas DataFrame by Date

If you are in a hurry, below are some quick examples of how to sort DataFrame by DateTime

# Quick examples of sort pandas dataframe by date

# Example 1: Sort DataFrame by date column
df.sort_values(by='Starting_dates', inplace = True) 

# Example 2: Sort DataFrame by date column in descending order
df.sort_values(by='Starting_dates', ascending = False, inplace = True) 

# Example 3: Sort multiple date columns
df.sort_values(by=['Starting_dates', 'ending_dates'], inplace = True)

# Example 4: Sort multiple date columns in descending order
df.sort_values(by=['Starting_dates', 'ending_dates'], ascending = False, inplace = True)

<a id="h-sort-pandas-dataframe-by-date"></a>

Sort Pandas DataFrame by Date

Following are the steps to sort a Pandas DataFrame by date or datetime column. Let's follow the steps

The first step create a DataFrame with a few rows and columns, execute the above examples, and validate the results. Our DataFrame contains column names Courses, Fee, Duration, Discount and 'Starting_dates'.

import pandas as pd
technologies = {
      'Courses':["Spark", "Hadoop", "pandas", "Python", "PySpark"],
     'Fee' :[20000,25000,30000, 22000, 24000],
     'Duration':['30days', '40days', '35days', '40days', '50days'],
     'Discount':[1000, 2500, 1500, 1200, 2300],
     'Starting_dates': ["10/10/2022",  "25/10/2022", "30/10/2022", "20/10/2022", "15/10/2022"]
     }
df = pd.DataFrame(technologies)
print(df)
print(df.dtype)

Yields below output.

<a id="h-convert-string-data-type-into-datetime-object"></a>

Convert String Data Type into Datetime Object

In order to sort Pandas DataFrame by its DateTime column, we need to make sure the date columns are in Datetime format. To use our DataFrame first we need to convert the string to datetime object.

Let's convert the specified column of DataFrame an object type to a datetime type using to_datetime() function.

# Convert object type to datetime object
df['Starting_dates'] = pd.to_datetime(df['Starting_dates'])
print(df.dtypes)

Yields below output.

As we can see we have successfully converted our data type to datetime.

Alternatively, we can convert the given column object type to string type by using pd.astype() function.

# Convert object type to datetime object
df["Starting_dates"] = df["Starting_dates"].astype('datetime64[ns]')
print(df.dtypes)

The output is the same as above.

<a id="sortingadataframebydateinpandas"></a>

Sort Pandas DataFrame by Date

Using the Pandas sort_values() function we can sort a given DataFrame by its DateTime column. For that, we need to set by param as a datetime column along with inplace = True, by default it will sort the given DataFrame in ascending order by specified column and return the existing DataFrame with sorted values. inplace = True is used to sort the DataFrame in place on the existing df.

# Sort DataFrame by date column
df.sort_values(by='Starting_dates', inplace = True)
print(df)

Yields below output.

# Output:
   Courses    Fee Duration  Discount Starting_dates
0    Spark  20000   30days      1000     10/10/2022
4  PySpark  24000   50days      2300     15/10/2022
3   Python  22000   40days      1200     20/10/2022
1   Hadoop  25000   40days      2500     25/10/2022
2   pandas  30000   35days      1500     30/10/2022

<a id="h-sort-pandas-by-date-in-descending-order"></a>

Sort Pandas by Date in Descending Order

Using the sort_values() function we can sort the given DataFrame over the Datetime column in descending order. It can be done by setting ascending param as False and pass into the sort_values() function. It sorts the DataFrame in descending order over the datetime column.

# Sort DataFrame by date column in descending order
df.sort_values(by='Starting_dates', ascending = False, inplace = True)
print(df)

Yields below output.

# Output:
   Courses    Fee Duration  Discount Starting_dates
2   pandas  30000   35days      1500     30/10/2022
1   Hadoop  25000   40days      2500     25/10/2022
3   Python  22000   40days      1200     20/10/2022
4  PySpark  24000   50days      2300     15/10/2022
0    Spark  20000   30days      1000     10/10/2022

<a id="h-sort-pandas-by-multiple-date-columns"></a>

Sort Pandas by Multiple Date Columns

Similarly, pass multiple columns to by param to sort pandas DataFrame by multiple columns by DateTime. Let's add another date column to the given DataFrame then, convert object type to DateTime object using to_datetime() function.

# Add new date column
ending_dates = ["10/11/2022", "5/12/2022", "5/12/2022", "1/12/2022", "5/12/2022"]
# Add column to DataFrame
df['ending_dates'] = ending_dates

# Apply to_datetime() function to multiple columns 
df[['Starting_dates', 'ending_dates']] = df[['Starting_dates', 'ending_dates']].apply(pd.to_datetime)
print(df.dtypes)
print(df)

Apply sort_values() function on multiple date columns of a given DataFrame, by default it will sort both date columns in ascending order and return the existing DataFrame with sorted values over Date columns.

# Sort multiple date columns
df.sort_values(by=['Starting_dates', 'ending_dates'], inplace = True)
print(df)

Yields below output.

# Output:
   Courses    Fee Duration  Discount Starting_dates ending_dates
0    Spark  20000   30days      1000     2022-10-10   2022-10-11
4  PySpark  24000   50days      2300     2022-10-15   2022-05-12
3   Python  22000   40days      1200     2022-10-20   2022-01-12
1   Hadoop  25000   40days      2500     2022-10-25   2022-05-12
2   pandas  30000   35days      1500     2022-10-30   2022-05-12

<a id="sortbymultipledatecolumnsindescendingorder"></a>

Sort Pandas by Multiple Date Columns in Descending Order

Set ascending param as False and pass it into this function along with specified multiple date columns. It sorts the given DataFrame in descending order by its date columns.

# Sort multiple date columns in descending order
df.sort_values(by=['Starting_dates', 'ending_dates'], ascending = False, inplace = True)
print(df)

Yields below output.

# Output:
  Courses    Fee Duration  Discount Starting_dates ending_dates
2   pandas  30000   35days      1500     2022-10-30   2022-05-12
1   Hadoop  25000   40days      2500     2022-10-25   2022-05-12
3   Python  22000   40days      1200     2022-10-20   2022-01-12
4  PySpark  24000   50days      2300     2022-10-15   2022-05-12
0    Spark  20000   30days      1000     2022-10-10   2022-10-11

<a id="h-faq-on-sort-pandas-dataframe-by-date-datetime"></a>

FAQ on Sort Pandas DataFrame by Date (Datetime)

To sort a DataFrame by a datetime column, you can use the sort_values() function and specify the datetime column. By default, it sorts in ascending order.

To sort a Pandas DataFrame by date in descending order, you can use the sort_values() function and set the ascending parameter to False.

If you have multiple date columns and want to sort by more than one, you can pass a list of column names to sort_values()

If your datetime column is not in datetime format, you need to first convert it to a datetime object before sorting. You can do this using pd.to_datetime(), which will convert the column to the appropriate datetime format.

To sort a Pandas DataFrame by a specific date range, you'll first need to filter the DataFrame to include only the rows within that date range. Once you have the filtered data, you can then sort it by the datetime column.

<a id="h-conclusion"></a>

Conclusion

In this article, I have explained how to sort Pandas DataFrame by Date using the sort_values() function. Also, learned sorting by single and multiple date columns, in ascending, descending, and even variable order with multiple examples. If your dates are not in date format use to_datatime() to convert to DateTime.

Happy learning!!

<a id="h-related-articles"></a>

Related Articles

• How to sort Pandas Series values
• How to Format Pandas Datetime?
• pandas Convert Datetime to Seconds
• Pandas Extract Year from Datetime
• Pandas DatetimeIndex Usage Explained
• Convert Pandas DatetimeIndex to String
• How to sort groups of Pandas DataFrame?
• Pandas Groupby Sort within Groups
• Pandas Series.sort_values() With Examples
• pandas.DataFrame.sort_values() – Examples
• Pandas Sort by Column Values DataFrame
• Pandas Convert Date (datetime) to String Format
• pandas DataFrame.sort_index() – Sort by Index
• How to Sort Multiple Columns in pandas DataFrame
• Select Pandas DataFrame Rows Between Two Dates
• How to Sort Multiple Columns in Pandas DataFrame

<a id="h-references"></a>

References

• https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.sort_values.html