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Articles by Rishikesh Kumar Rishi
Page 18 of 102
Python – Pandas Dataframe.rename()
It's quite simple to rename a DataFrame column name in Pandas. All that you need to do is to use the rename() method and pass the column name that you want to change and the new column name. Let's take an example and see how it's done.StepsCreate a two-dimensional, size-mutable, potentially heterogeneous tabular data, df.Print the input DataFrame, df.Use rename() method to rename the column name. Here, we will rename the column "x" with its new name "new_x".Print the DataFrame with the renamed column.Example import pandas as pd df = pd.DataFrame( { "x": [5, 2, ...
Read MoreHow to access a group of rows in a Pandas DataFrame?
To access a group of rows in a Pandas DataFrame, we can use the loc() method. For example, if we use df.loc[2:5], then it will select all the rows from 2 to 5.StepsCreate a two-dimensional, size-mutable, potentially heterogeneous tabular data, df.Print the input DataFrame, df.Use df.loc[2:5] to select the rows from 2 to 5.Print the DataFrame.Example import pandas as pd df = pd.DataFrame( { "x": [5, 2, 7, 0, 7, 0, 5, 2], "y": [4, 7, 5, 1, 5, 1, 4, 7], "z": [9, 3, 5, 1, 5, 1, 9, 3] } ) print "Input DataFrame is:", df df = df.loc[2:5] print "New DataFrame:", dfOutput Input DataFrame is: x y z 0 5 4 9 1 2 7 3 2 7 5 5 3 0 1 1 4 7 5 5 5 0 1 1 6 5 4 9 7 2 7 3 New DataFrame: x y z 2 7 5 5 3 0 1 1 4 7 5 5 5 0 1 1
Read MoreDelete the first three rows of a DataFrame in Pandas
To delete the first three rows of a DataFrame in Pandas, we can use the iloc() method.StepsCreate a two-dimensional, size-mutable, potentially heterogeneous tabular data, df.Print the input DataFrame, df.Delete the first three rows using df.iloc[3:].Print the updated DataFrame.Example import pandas as pd df = pd.DataFrame( { "x": [5, 2, 7, 0, 7, 0, 5, 2], "y": [4, 7, 5, 1, 5, 1, 4, 7], "z": [9, 3, 5, 1, 5, 1, 9, 3] } ) print "Input DataFrame is:", df df = df.iloc[3:] print "After deleting the first 3 rows: ", dfOutput Input DataFrame is: x y z 0 5 4 9 1 2 7 3 2 7 5 5 3 0 1 1 4 7 5 5 5 0 1 1 6 5 4 9 7 2 7 3 After deleting the first 3 rows: x y z 3 0 1 1 4 7 5 5 5 0 1 1 6 5 4 9 7 2 7 3
Read MoreHow to convert a DataFrame into a dictionary in Pandas?
To convert a Pandas DataFrame into a dictionary, we can use the to_dict() method. Let's take an example and see how it's done.StepsCreate two-dimensional, size-mutable, potentially heterogeneous tabular data, df.Print the input DataFrame, df.Convert the DataFrame into a dictionary using to_dict() method and print it.Example import pandas as pd df = pd.DataFrame( { "x": [5, 2, 7, 0], "y": [4, 7, 5, 1], "z": [9, 3, 5, 1] } ) print "Input DataFrame is:", df print "Convert DataFrame into dictionary: ", df.to_dict()Output Input DataFrame is: x y z 0 5 4 9 1 2 7 3 2 7 5 5 3 0 1 1 Convert DataFrame into dictionary: {'x': {0: 5, 1: 2, 2: 7, 3: 0}, 'y': {0: 4, 1: 7, 2: 5, 3: 1}, 'z': {0: 9, 1: 3, 2: 5, 3: 1}}
Read MoreSelect DataFrame rows between two index values in Python Pandas
We can slice a Pandas DataFrame to select rows between two index values. Let's take an example and see how it's done.StepsCreate a two-dimensional, size-mutable, potentially heterogeneous tabular data, df.Print the input DataFrame, df.Initialize a variable for lower limit of the index.Initialize another variable for upper limit of the index.Use df[index_lower_limit: index_upper_limit] to print the DataFrame in range index.Exampleimport pandas as pd df = pd.DataFrame( { "x": [5, 2, 7, 0], "y": [4, 7, 5, 1], "z": [9, ...
Read MoreSelecting with complex criteria from a Pandas DataFrame
We can use different criteria to compare all the column values of a Pandas DataFrame. We can perform comparison operations like df[col]2, then it will check all the values from col and compare whether they are greater than 2. For all the column values, it will return True if the condition holds, else False. Let's take an example and see how it's done.StepsCreate a two-dimensional, size-mutable, potentially heterogeneous tabular data, df.Print the input DataFrame, df.Initialize a variable col, with a column name.Perform some comparison operations.Print the resultant DataFrame.Example import pandas as pd df = pd.DataFrame( ...
Read MoreGroup-by and Sum in Python Pandas
To find group-by and sum in Python Pandas, we can use groupby(columns).sum().StepsCreate a two-dimensional, size-mutable, potentially heterogeneous tabular data, df.Print the input DataFrame, df.Find the groupby sum using df.groupby().sum(). This function takes a given column and sorts its values. After that, based on the sorted values, it also sorts the values of other columns.Print the groupby sum.Exampleimport pandas as pd df = pd.DataFrame( { "Apple": [5, 2, 7, 0], "Banana": [4, 7, 5, 1], "Carrot": [9, 3, 5, 1] } ) print "Input DataFrame 1 ...
Read MoreHow to get column index from column name in Python Pandas?
To get column index from column name in Python Pandas, we can use the get_loc() method.Steps −Create a two-dimensional, size-mutable, potentially heterogeneous tabular data, df.Print the input DataFrame, df.Find the columns of DataFrame, using df.columns.Print the columns from Step 3.Initialize a variable column_name.Get the location, i.e., of index for column_name.Print the index of the column_name.Example −import pandas as pd df = pd.DataFrame( { "x": [5, 2, 7, 0], "y": [4, 7, 5, 1], "z": [9, 3, 5, 1] } ) print"Input DataFrame 1 is:", df columns = ...
Read MoreHow to find the common elements in a Pandas DataFrame?
To find the common elements in a Pandas DataFrame, we can use the merge() method with a list of columnsStepsCreate a two-dimensional, size-mutable, potentially heterogeneous tabular data, df1.Print the input DataFrame, df1.Create another two-dimensional tabular data, df2.Print the input DataFrame, df2.Find the common elements using merge() method.Print the common DataFrame.Exampleimport pandas as pd df1 = pd.DataFrame( { "x": [5, 2, 7, 0], "y": [4, 7, 5, 1], "z": [9, 3, 5, 1] } ) df2 = ...
Read MoreHow to sort multiple columns of a Pandas DataFrame?
To sort multiple columns of a Pandas DataFrame, we can use the sort_values() method.StepsCreate a two-dimensional, size-mutable, potentially heterogeneous tabular data, df.Print the input DataFrame, df.Initialize a variable col to sort the column.Print the sorted DataFrame.Example Live Demoimport pandas as pd df = pd.DataFrame( { "x": [5, 2, 7, 0], "y": [4, 7, 5, 1], "z": [9, 3, 5, 1] } ) print "Input DataFrame is:", df col = ["x", "y"] df = df.sort_values(col, ascending=[False, True]) print "After sorting column ", col, "DataFrame is:", dfOutputInput DataFrame is: x y z 0 5 4 9 1 2 7 3 2 7 5 5 3 0 1 1 After sorting column ['x', 'y'] DataFrame is: x y z 2 7 5 5 0 5 4 9 1 2 7 3 3 0 1 1
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