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'])
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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)
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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.
