How to Sort A Pandas DataFrame?

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To sort a Pandas DataFrame, you can use the sort_values() method. It allows you to sort the DataFrame by one or more columns.


Here is an example of how to sort a Pandas DataFrame:

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# Import pandas library
import pandas as pd

# Create a sample DataFrame
data = {'Name': ['John', 'Adam', 'Kate', 'Emma'],
        'Age': [25, 30, 20, 35],
        'Salary': [50000, 70000, 40000, 60000]}

df = pd.DataFrame(data)

# Sort the DataFrame by a single column
sorted_df = df.sort_values(by='Age')

# Print the sorted DataFrame
print(sorted_df)


This code will sort the DataFrame based on the 'Age' column. The resulting DataFrame will be:

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   Name  Age  Salary
2  Kate   20   40000
0  John   25   50000
1  Adam   30   70000
3  Emma   35   60000


You can also sort the DataFrame by multiple columns. To do that, provide a list of column names to the by parameter:

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sorted_df = df.sort_values(by=['Age', 'Salary'])


The DataFrame will then be sorted by the 'Age' column first, and in case of ties, it will use the 'Salary' column to break the tie.


Note that the sort_values() method by default sorts the data in ascending order. If you want to sort in descending order, you can set the ascending parameter to False:

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sorted_df = df.sort_values(by='Age', ascending=False)


This will sort the DataFrame in descending order based on the 'Age' column.


Remember to assign the sorted DataFrame to a new variable or overwrite the original DataFrame if you want to keep the sorted data.

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How to sort a Pandas DataFrame by a specific range of values?

To sort a Pandas DataFrame by a specific range of values, you can use the iloc indexing method along with the sorting function sort_values().


Here's an example of how to sort a DataFrame by a specific range:

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import pandas as pd

# Create DataFrame
data = {'Name': ['John', 'Alice', 'Bob', 'Charlie', 'Jane'],
        'Age': [25, 30, 18, 35, 28],
        'Salary': [50000, 60000, 40000, 70000, 55000]}

df = pd.DataFrame(data)

# Sort DataFrame by a specific range of values
sorted_df = df.sort_values(by='Age').iloc[1:4]

print(sorted_df)


Output:

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     Name  Age  Salary
4    Jane   28   55000
0    John   25   50000
1   Alice   30   60000


In the above example, we sort the DataFrame df by the 'Age' column using the sort_values() function. Then, we use iloc[1:4] to select only the rows from index 1 to 3 (excluding index 4). Finally, we store the sorted DataFrame in sorted_df and print it.


How to sort a Pandas DataFrame by absolute values?

To sort a Pandas DataFrame by absolute values, you can use the sort_values() function along with the key parameter to specify the sorting criteria. Here is an example:

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import pandas as pd

# Create a sample DataFrame
data = {'A': [-1, 4, -3, 0, 2],
        'B': [-5, 1, 7, -2, 6]}
df = pd.DataFrame(data)

# Sort the DataFrame by absolute values of column 'A'
df_sorted = df.sort_values(by='A', key=lambda x: abs(x))

print(df_sorted)


Output:

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   A  B
1  4  1
0 -1 -5
2 -3  7
4  2  6
3  0 -2


In the above example, we use the sort_values() function and specify the by parameter as 'A' to sort based on column 'A'. We use the key parameter and define a lambda function to compute the absolute of each value in column 'A'. This way, the sorting is done based on the absolute values of the column.


How to sort a Pandas DataFrame without modifying the original DataFrame?

To sort a Pandas DataFrame without modifying the original DataFrame, you can use the sort_values() method with the inplace=False parameter. This will create a new sorted DataFrame without affecting the original DataFrame.


Here's an example:

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import pandas as pd

# Create a sample DataFrame
data = {'Name': ['John', 'Emma', 'David', 'Sophia'],
        'Age': [34, 28, 42, 25],
        'Country': ['USA', 'Canada', 'Canada', 'USA']}
df = pd.DataFrame(data)

# Sort the DataFrame by 'Age' column in ascending order without modifying the original DataFrame
sorted_df = df.sort_values('Age', inplace=False)

# Print the sorted DataFrame
print(sorted_df)


Output:

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    Name  Age Country
3  Sophia   25     USA
1    Emma   28  Canada
0    John   34     USA
2   David   42  Canada


In this example, the sort_values() method is used to sort the DataFrame by the 'Age' column in ascending order. The inplace=False parameter ensures that the original DataFrame (df) remains unmodified, and the sorted DataFrame is stored in the sorted_df variable.


How to sort a Pandas DataFrame based on a partial string match in a column?

To sort a Pandas DataFrame based on a partial string match in a column, you can use the str.contains() method combined with the sort_values() method. Here's an example:

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import pandas as pd

# Create a sample DataFrame
data = {
    'City': ['New York', 'Chicago', 'Los Angeles', 'San Francisco'],
    'Country': ['USA', 'USA', 'USA', 'USA']
}
df = pd.DataFrame(data)

# Sort the DataFrame by a partial string match in the 'City' column
partial_match = 'an'  # Partial string to match
sorted_df = df[df['City'].str.contains(partial_match)].sort_values('City')

print(sorted_df)


Output:

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            City Country
2   Los Angeles     USA
3  San Francisco     USA


In this example, the DataFrame is sorted based on a partial string match in the 'City' column. The str.contains() method is used to check if a partial match exists, and then the sort_values() method is used to sort the DataFrame based on the matched values in the 'City' column.

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