JIYIK CN >

Pandas DataFrame.sort_values() method sorts DataFramethe values ​​in the specified column of the caller along any index in ascending or descending order.

pandas.DataFrame.sort_values()grammar

DataFrame.sort_values(
    by,
    axis=0,
    ascending=True,
    inplace=False,
    kind="quicksort",
    na_position="last",
    ignore_index=False,
)

parameter

Return Value

If inplaceis True, return the sorted DataFrame; otherwise is None.

Example Code: Sorting a DataFrame Using Pandas pandas.DataFrame.sort_values()Sorting Based on a Single Column

import pandas as pd

dates=['April-10', 
       'April-11', 
       'April-12', 
       'April-13',
       'April-14',
       'April-16']
sales=[200,300,400,200,300,300]
prices=[3, 1, 2, 4,3,2]

df = pd.DataFrame({'Date':dates ,
                   'Sales':sales ,
                   'Price': prices})
print("Before Sorting:")
print(df)
sorted_df=df.sort_values(by=['Price'])
print("After Sorting:")
print(sorted_df)

Output:

Before Sorting:
       Date  Sales  Price
0  April-10    200      3
1  April-11    300      1
2  April-12    400      2
3  April-13    200      4
4  April-14    300      3
5  April-16    300      2
After Sorting:
       Date  Sales  Price
       Date  Sales  Price
1  April-11    300      1
2  April-12    400      2
5  April-16    300      2
0  April-10    200      3
4  April-14    300      3
3  April-13    200      4

It sorts Pricethe DataFrame in ascending order (default) based on the values ​​in the column .df

The index in the sorted DataFrame remains the same as the index in the original DataFrame.

DataFrameIf you prefer to use the new index column in the sorted , you can set it ignore_index(introduced in version 1.0.0) to True.

import pandas as pd

dates = ["April-10", "April-11", "April-12", "April-13", "April-14", "April-16"]
sales = [200, 300, 400, 200, 300, 300]
prices = [3, 1, 2, 4, 3, 2]

df = pd.DataFrame({"Date": dates, "Sales": sales, "Price": prices})
print("Before Sorting:")
print(df)
sorted_df = df.sort_values(by=["Price"], ignore_index=True)
print("After Sorting:")

Output:

Before Sorting:
       Date  Sales  Price
0  April-10    200      3
1  April-11    300      1
2  April-12    400      2
3  April-13    200      4
4  April-14    300      3
5  April-16    300      2
After Sorting:
       Date  Sales  Price
0  April-11    300      1
1  April-12    400      2
2  April-16    300      2
3  April-10    200      3
4  April-14    300      3
5  April-13    200      4

Here, we use ignore_index=Trueto assign new indices to the rows and ignore DataFramethe original indices.

Example Code: DataFrame.sort_values()Sort DataFrame Based on Multiple Columns Using Pandas

import pandas as pd

dates=['April-10', 
       'April-11', 
       'April-12', 
       'April-13',
       'April-14',
       'April-16']
sales=[200,300,400,200,300,300]
prices=[3, 1, 2, 4,3,2]

df = pd.DataFrame({'Date':dates ,
                   'Sales':sales ,
                   'Price': prices})
print("Before Sorting:")
print(df)
df.sort_values(by=['Sales','Price'],
               ignore_index=True,
               inplace=True)
print("After Sorting:")
print(df)

Output:

Before Sorting:
       Date  Sales  Price
0  April-10    200      3
1  April-11    300      1
2  April-12    400      2
3  April-13    200      4
4  April-14    300      3
5  April-16    300      2
After Sorting:
       Date  Sales  Price
0  April-10    200      3
1  April-13    200      4
2  April-11    300      1
3  April-16    300      2
4  April-14    300      3
5  April-12    400      2

Here, first Salessort is in ascending order, and then for each Sales, Pricethe is also sorted in ascending order.

In df, 200is Salesthe minimum value of the column, and 3is the minimum value of the column whose Salesvalue is .200Price

So, the rows Saleswith in columns 200and Pricein columns are at the front.3

Because inplace=True, after calling sort_values()the function, the original DataFrameis modified in place.

Example Code: DataFrame.sort_values()Sort DataFrame in Descending Order using Pandas

import pandas as pd

dates=['April-10', 
       'April-11', 
       'April-12', 
       'April-13',
       'April-14',
       'April-16']
sales=[200,300,400,200,300,300]
prices=[3, 1, 2, 4,3,2]

df = pd.DataFrame({'Date':dates ,
                   'Sales':sales ,
                   'Price': prices})
print("Before Sorting:")
print(df)
sorted_df=df.sort_values(by=['Sales'],
                         ignore_index=True,
                         ascending=False)
print("After Sorting:")
print(sorted_df)

Output:

Before Sorting:
       Date  Sales  Price
0  April-10    200      3
1  April-11    300      1
2  April-12    400      2
3  April-13    200      4
4  April-14    300      3
5  April-16    300      2
After Sorting:
       Date  Sales  Price
0  April-12    400      2
1  April-11    300      1
2  April-14    300      3
3  April-16    300      2
4  April-10    200      3
5  April-13    200      4

It sorts Salesthe DataFrame in descending order based on the numerical value of the column .df

400is Salesthe maximum value in the column, so that entry will be placed at the top and the other rows will be sorted accordingly.

Example Code: DataFrame.sort_values()Sort DataFrame using Pandas, NaNputting

import pandas as pd

dates=['April-10', 
       'April-11', 
       'April-12', 
       'April-13',
       'April-14',
       'April-16']
sales=[200,300,400,200,300,300]
prices=[3, 1, 2, 4,3,2]

df = pd.DataFrame({'Date':dates ,
                   'Sales':sales ,
                   'Price': prices})
print("Before Sorting:")
print(df)
sorted_df=df.sort_values(by=['Price'],ignore_index=True,na_position='first')
print("After Sorting:")
print(sorted_df)

Output:

Before Sorting:
       Date  Sales  Price
0  April-10    200    NaN
1  April-11    300    1.0
2  April-12    400    2.0
3  April-13    200    4.0
4  April-14    300    3.0
5  April-16    300    NaN
After Sorting:
       Date  Sales  Price
0  April-10    200    NaN
1  April-16    300    NaN
2  April-11    300    1.0
3  April-12    400    2.0
4  April-14    300    3.0
5  April-13    200    4.0

By default, values ​​are placed last NaNafter sorting .DataFrame

But by setting na_position=first, we can NaNput the value DataFrameat the beginning of .