How to Sort CSV by a single column in Python ?

To sort a CSV file by a single column in Python, use the sort_values() method from Pandas. This method allows you to sort a DataFrame by specifying the column name and sort order.

Syntax

DataFrame.sort_values(by, axis=0, ascending=True, inplace=False, na_position='last')

Parameters

• by ? Column name to sort by
• axis ? 0 for rows, 1 for columns (default: 0)
• ascending ? True for ascending, False for descending (default: True)
• inplace ? Modify original DataFrame or return new one (default: False)
• na_position ? Where to place NaN values: 'first' or 'last' (default: 'last')

Example

Let's create a sample DataFrame with car sales data and sort it by different columns ?

import pandas as pd

# Create sample car sales data
data = {
    'Car': ['BMW', 'Audi', 'Lexus', 'Jaguar', 'Mustang', 'Lamborghini'],
    'Date_of_Purchase': ['10/10/2020', '10/12/2020', '10/17/2020', 
                        '10/16/2020', '10/19/2020', '10/22/2020'],
    'Reg_Price': [1000, 750, 1250, 1500, 1100, 1000]
}

df = pd.DataFrame(data)
print("Original DataFrame:")
print(df)

Original DataFrame:
          Car Date_of_Purchase  Reg_Price
0         BMW        10/10/2020       1000
1        Audi        10/12/2020        750
2       Lexus        10/17/2020       1250
3      Jaguar        10/16/2020       1500
4     Mustang        10/19/2020       1100
5  Lamborghini        10/22/2020       1000

Sorting by Car Names

Sort the DataFrame alphabetically by the 'Car' column ?

import pandas as pd

data = {
    'Car': ['BMW', 'Audi', 'Lexus', 'Jaguar', 'Mustang', 'Lamborghini'],
    'Date_of_Purchase': ['10/10/2020', '10/12/2020', '10/17/2020', 
                        '10/16/2020', '10/19/2020', '10/22/2020'],
    'Reg_Price': [1000, 750, 1250, 1500, 1100, 1000]
}

df = pd.DataFrame(data)

# Sort by Car column (ascending)
sorted_by_car = df.sort_values("Car")
print("Sorted by Car Names:")
print(sorted_by_car)

Sorted by Car Names:
          Car Date_of_Purchase  Reg_Price
1        Audi        10/12/2020        750
0         BMW        10/10/2020       1000
3      Jaguar        10/16/2020       1500
5  Lamborghini        10/22/2020       1000
2       Lexus        10/17/2020       1250
4     Mustang        10/19/2020       1100

Sorting by Price

Sort the DataFrame by registration price in ascending order ?

import pandas as pd

data = {
    'Car': ['BMW', 'Audi', 'Lexus', 'Jaguar', 'Mustang', 'Lamborghini'],
    'Date_of_Purchase': ['10/10/2020', '10/12/2020', '10/17/2020', 
                        '10/16/2020', '10/19/2020', '10/22/2020'],
    'Reg_Price': [1000, 750, 1250, 1500, 1100, 1000]
}

df = pd.DataFrame(data)

# Sort by Registration Price
sorted_by_price = df.sort_values("Reg_Price")
print("Sorted by Registration Price:")
print(sorted_by_price)

Sorted by Registration Price:
          Car Date_of_Purchase  Reg_Price
1        Audi        10/12/2020        750
0         BMW        10/10/2020       1000
5  Lamborghini        10/22/2020       1000
4     Mustang        10/19/2020       1100
2       Lexus        10/17/2020       1250
3      Jaguar        10/16/2020       1500

Sorting in Descending Order

To sort in descending order, set ascending=False ?

import pandas as pd

data = {
    'Car': ['BMW', 'Audi', 'Lexus', 'Jaguar', 'Mustang', 'Lamborghini'],
    'Reg_Price': [1000, 750, 1250, 1500, 1100, 1000]
}

df = pd.DataFrame(data)

# Sort by price in descending order
sorted_desc = df.sort_values("Reg_Price", ascending=False)
print("Sorted by Price (Descending):")
print(sorted_desc)

Sorted by Price (Descending):
          Car  Reg_Price
3      Jaguar       1500
2       Lexus       1250
4     Mustang       1100
0         BMW       1000
5  Lamborghini       1000
1        Audi        750

Conclusion

Use sort_values() to sort a DataFrame by any column. Set ascending=False for descending order, and use inplace=True to modify the original DataFrame directly.