Sorting data is an essential task in data analysis, and Pandas provides a powerful method called sort_values() to sort DataFrames based on one or more columns. In this article, we will learn how to use sort_value method to Sort by Column a Pandas DataFrame and with the different parameters that can be used to customize the sorting behavior.\u00a0<\/p>\n\n\n\n
The sort_values() function in Pandas allows us to sort DataFrames on the basis of one or more columns.<\/strong> It takes several parameters that can define the sorting behavior, such as: the columns to sort by, the sorting order (ascending or descending), and handling null values.<\/p>\n\n\n\n Let us now explore the various ways to use the sort_values function to sort a DataFrame based on single and multiple columns.<\/p>\n\n\n\n Sorting data in a DataFrame in ascending order is the default behavior of the sort_values() function. This means that when no sorting order is defined, the sort_values will sort the DataFrame in Ascending Order.\u00a0<\/p>\n\n\n\n To sort a DataFrame by a single column, we can simply pass the column name to the by parameter of the sort_values() function.<\/strong> <\/p>\n\n\n\n For example, let’s sort a DataFrame called df by the ‘Age\u2019’ column with implementation in Python:<\/p>\n\n\n\n Output:<\/strong><\/p>\n\n\n\n Sorting by multiple columns allows us to define a hierarchical sorting order. We can pass a list of column names to the by parameter to sort the DataFrame based on multiple columns. The sorting is performed sequentially, with the first column taking precedence over the second, and so on. This simply means, in the case of clashes in the first column, the second column will be used as a second preference.<\/p>\n\n\n\n Let us consider an example, let’s sort the DataFrame df by the ‘Age\u2019 and ‘Name’ columns:<\/p>\n\n\n\n Output:<\/strong><\/p>\n\n\n\n By default, the sort_values() function places null values at the end of the sorted DataFrame. However, we can change this behavior by setting the na_position parameter to ‘first’ to place null values at the beginning. <\/p>\n\n\n\n For example, let us try to sort the DataFrame df by the ‘Age\u2019’ column with null values first:<\/p>\n\n\n\n Output:<\/strong><\/p>\n\n\n\n We can also sort the DataFrame in similar ways in Descending Order. To do this we simply need to set the ascending parameter to False. This will reverse the sorting order of the specified column(s).<\/p>\n\n\n\n Let us try a simple example:<\/p>\n\n\n\n Output:<\/strong><\/p>\n\n\n\n We can also use custom sorting algorithms to sort a DataFrame. The sort_values() function in Pandas provides different sorting algorithms to choose from. By default, the sorting algorithm used for the sort_values function is ‘quicksort’. <\/strong>We can change this to any algorithm according to our needs. <\/p>\n\n\n\n For example, let us try to sort a DataFrame using the \u2018merge sort\u2019:<\/p>\n\n\n\n Output:<\/strong><\/p>\n\n\n\nSorting in Ascending Order<\/strong><\/h2>\n\n\n\n
Sorting by a Single Column <\/strong><\/h3>\n\n\n\n
import pandas as pd\n\ndata = {\n "Name": ["John", "Emma", "Michael"],\n "Age": [45, 30, 35],\n "City": ["New York", "London", "Paris"]\n}\ndf = pd.DataFrame(data)\n\n# Display the original DataFrame\nprint('Original DataFrame:\\n', df)\n\n# Sort the DataFrame by the 'Age' column\ndf = df.sort_values(by='Age')\n\n# Display the sorted DataFrame\nprint('Sorted DataFrame:\\n', df)<\/pre><\/div>\n\n\n\nOriginal DataFrame:\n Name Age City\n0 John 45 New York\n1 Emma 30 London\n2 Michael 35 Paris\n\nSorted DataFrame:\n Name Age City\n1 Emma 30 London\n2 Michael 35 Paris\n0 John 45 New York\n<\/code><\/pre>\n\n\n\nSorting by Multiple Columns<\/strong> <\/h3>\n\n\n\n
import pandas as pd\n\ndata = {\n "Name": ["John", "Zmma", "Michael"],\n "Age": [45, 30, 35],\n "City": ["New York", "London", "Paris"]\n}\ndf = pd.DataFrame(data)\n\n# Display the original DataFrame\nprint('Original DataFrame:\\n', df)\n\n# Sort the DataFrame by multiple columns ('Age' and 'Name')\ndf = df.sort_values(by=['Age', 'Name'])\n\n# Display the sorted DataFrame\nprint('Sorted DataFrame:\\n', df)<\/pre><\/div>\n\n\n\nOriginal DataFrame:\n Name Age City\n0 John 45 New York\n1 Zmma 30 London\n2 Michael 35 Paris\n\nSorted DataFrame:\n Name Age City\n1 Zmma 30 London\n2 Michael 35 Paris\n0 John 45 New York\n<\/code><\/pre>\n\n\n\nSorting with Null Values <\/strong><\/h3>\n\n\n\n
import pandas as pd\n\ndata = {\n "Name": ["John", "Emma", "Michael"],\n "Age": [45, 30, None],\n "City": ["New York", "London", "Paris"]\n}\ndf = pd.DataFrame(data)\n\n# Display the original DataFrame\nprint('Original DataFrame:\\n', df)\n\n# Sort the DataFrame by column ('Age') with null values placed first\ndf = df.sort_values(by='Age', na_position='first')\n\n# Display the sorted DataFrame\nprint('Sorted DataFrame:\\n', df)<\/pre><\/div>\n\n\n\nOriginal DataFrame:\n Name Age City\n0 John 45.0 New York\n1 Emma 30.0 London\n2 Michael NaN Paris\n\nSorted DataFrame:\n Name Age City\n2 Michael NaN Paris\n1 Emma 30.0 London\n0 John 45.0 New York\n<\/code><\/pre>\n\n\n\nSorting in Descending Order <\/strong><\/h2>\n\n\n\n
import pandas as pd\n\ndata = {\n "Name": ["John", "Emma", "Michael"],\n "Age": [45, 30, 55],\n "City": ["New York", "London", "Paris"]\n}\ndf = pd.DataFrame(data)\n\n# Display the original DataFrame\nprint('Original DataFrame:\\n', df)\n\n# Sort the DataFrame by column ('Age') in descending order\ndf = df.sort_values(by='Age', ascending=False)\n\n# Display the sorted DataFrame\nprint('Sorted DataFrame:\\n', df)<\/pre><\/div>\n\n\n\nOriginal DataFrame:\n Name Age City\n0 John 45 New York\n1 Emma 30 London\n2 Michael 55 Paris\n\nSorted DataFrame:\n Name Age City\n2 Michael 55 Paris\n0 John 45 New York\n1 Emma 30 London\n<\/code><\/pre>\n\n\n\nSorting with Custom Sorting Algorithm<\/strong><\/h2>\n\n\n\n
import pandas as pd\n\ndata = {\n "Name": ["John", "Emma", "Michael"],\n "Age": [45, 30, 55],\n "City": ["New York", "London", "Paris"]\n}\ndf = pd.DataFrame(data)\n\n# Display the original DataFrame\nprint('Original DataFrame:\\n', df)\n\n# Sort the DataFrame by column ('Age') using mergesort\ndf = df.sort_values(by='Age', kind='mergesort')\n\n# Display the sorted DataFrame\nprint('Sorted DataFrame:\\n', df)<\/pre><\/div>\n\n\n\nOriginal DataFrame:\n Name Age City\n0 John 45 New York\n1 Emma 30 London\n2 Michael 55 Paris\n\nSorted DataFrame:\n Name Age City\n1 Emma 30 London\n0 John 45 New York\n2 Michael 55 Paris<\/code><\/pre>\n\n\n\n