To make a multi-index in Pandas, we can use groupby with list of columns.StepsCreate a two-dimensional, size-mutable, potentially heterogeneous tabular data, df.Print the input DataFrame.Print the index of DataFrame count.Use groupby to get different levels of a hierarchical index and count it.Print the mulitindex set in step 4.Example Live Demoimport pandas ... Read More

To convert a Pandas DataFrame to a NumPy array, we can use to_numpy().StepsCreate a two-dimensional, size-mutable, potentially heterogeneous tabular data, df.Print the input DataFrame.Print the NumPy array of the given array, using df.to_numpy().Print the NumPy array of the given array for a specific column, using df['x'].to_numpy().Example Live Demoimport pandas as pd ... Read More

To count the NaN values in a column in a Pandas DataFrame, we can use the isna() method with sum.StepsCreate a series, s, one-dimensional ndarray with axis labels (including time series).Print the series, s.Count the number of NaN present in the series.Create a two-dimensional, size-mutable, potentially heterogeneous tabular data, df.Print ... Read More

To delete a DataFrame row in Pandas based on column value, we can take the following Steps −StepsCreate a two-dimensional, size-mutable, potentially heterogeneous tabular data, df.Print the input DataFrame.Here, we will delete the row from the DataFrame that contains 0 in its Z-column, using df=df[df.z != 0]Print the updated DataFrame, ... Read More

Let's take an example to understand the difference between iloc and loc. Basically loc[0] returns the value present at 0 index, whereas iloc[0] returns the value present at the first location of a series.StepsCreate a one-dimensional ndarray with axis labels (including time series).Print the input series.Use loc[0] to print the ... Read More

To write a Pandas DataFrame to CSV file, we can take the following Steps −StepsCreate a two-dimensional, size-mutable, potentially heterogeneous tabular data, df.Print the input DataFrame.Use df.to_csv to save the values of the DataFrame to a CSV (comma-separated values) file.Example Live Demoimport pandas as pd df = pd.DataFrame(    { ... Read More

To select the rows from a Pandas DataFrame based on input values, we can use the isin() method.StepsCreate a two-dimensional, size-mutable, potentially heterogeneous tabular data, df.Print the input DataFrame.Create a list of values for selection of rows.Print the selected rows with the given values.Next, print the rows that were not ... Read More

To create a Pandas DataFrame by appending one row at a time, we can iterate in a range and add multiple columns data in it.StepsCreate a two-dimensional, size-mutable, potentially heterogeneous tabular data, df.Print the input DataFrame.Iterate in a range of 10.Assign values at different index with numbers.Print the created DataFrame.Example Live ... Read More

To change the order of DataFrame columns, we can take the following Steps −StepsMake two-dimensional, size-mutable, potentially heterogeneous tabular data, df.Print the input DataFrame.Get the list of DataFrame columns, using df.columns.tolist()Change the order of DataFrame columns.Modify the order of columns of the DataFrame.Print the DataFrame after changing the columns order.Example Live ... Read More

To get a list of Pandas DataFrame column headers, we can use df.columns.values.StepsCreate a two-dimensional, size-mutable, potentially heterogeneous tabular data, df.Print the input DataFrame.Print the list of df.columns.values output.Example Live Demoimport pandas as pd df = pd.DataFrame(    {       "x": [5, 2, 1, 9],       ... Read More