Pandas DataFrame Methods: idxmax(), idxmin(), reindex(), reindex_like(), rename(), rename_axis() – Part 9

The Pandas DataFrame has several Reindexing/Selection/Label Manipulations methods. When applied to a DataFrame, these methods evaluate, modify the elements and return the results.

This is Part 9 of the DataFrame methods series:

• Part 1 focuses on the DataFrame methods abs(), all(), any(), clip(), corr(), and corrwith().
• Part 2 focuses on the DataFrame methods count(), cov(), cummax(), cummin(), cumprod(), cumsum().
• Part 3 focuses on the DataFrame methods describe(), diff(), eval(), kurtosis().
• Part 4 focuses on the DataFrame methods mad(), min(), max(), mean(), median(), and mode().
• Part 5 focuses on the DataFrame methods pct_change(), quantile(), rank(), round(), prod(), and product().
• Part 6 focuses on the DataFrame methods add_prefix(), add_suffix(), and align().
• Part 7 focuses on the DataFrame methods at_time(), between_time(), drop(), drop_duplicates() and duplicated().
• Part 8 focuses on the DataFrame methods equals(), filter(), first(), last(), head(), and tail()
• Part 9 focuses on the DataFrame methods equals(), filter(), first(), last(), head(), and tail()
• Part 10 focuses on the DataFrame methods reset_index(), sample(), set_axis(), set_index(), take(), and truncate()
• Part 11 focuses on the DataFrame methods backfill(), bfill(), fillna(), dropna(), and interpolate()
• Part 12 focuses on the DataFrame methods isna(), isnull(), notna(), notnull(), pad() and replace()
• Part 13 focuses on the DataFrame methods drop_level(), pivot(), pivot_table(), reorder_levels(), sort_values() and sort_index()
• Part 14 focuses on the DataFrame methods nlargest(), nsmallest(), swap_level(), stack(), unstack() and swap_axes()
• Part 15 focuses on the DataFrame methods melt(), explode(), squeeze(), to_xarray(), t() and transpose()
• Part 16 focuses on the DataFrame methods append(), assign(), compare(), join(), merge() and update()
• Part 17 focuses on the DataFrame methods asfreq(), asof(), shift(), slice_shift(), tshift(), first_valid_index(), and last_valid_index()
• Part 18 focuses on the DataFrame methods resample(), to_period(), to_timestamp(), tz_localize(), and tz_convert()
• Part 19 focuses on the visualization aspect of DataFrames and Series via plotting, such as plot(), and plot.area().
• Part 20 focuses on continuing the visualization aspect of DataFrames and Series via plotting such as hexbin, hist, pie, and scatter plots.
• Part 21 focuses on converting one data type to/from another data type.
• Part 22 focuses on converting one data type to/from another data type.
• Part 23 focuses on converting one data type to/from another data type.
• Part 24 focuses on converting one data type to/from another data type.
• Part 25 focuses on converting one data type to/from another data type.

Preparation

Before any data manipulation can occur, one (1) new library will require installation:

• The Pandas library enables access to/from a DataFrame.

To install this library, navigate to an IDE terminal. At the command prompt ($), execute the code below. For the terminal used in this example, the command prompt is a dollar sign ($). Your terminal prompt may be different.

$ pip install pandas

Hit the <Enter> key on the keyboard to start the installation process.

If the installation was successful, a message displays in the terminal indicating the same.

Feel free to view the PyCharm installation guide for the required library.

• How to Install Pandas on PyCharm

Add the following code to the top of each code snippet. This snippet will allow the code in this article to run error-free.

import pandas as pd

DataFrame idxmax()

The idxmax() method checks for and returns the index of the first occurrence of the maximum index(es) over a selected axis.

The syntax for this method is as follows:

DataFrame.idxmax(axis=0, skipna=True)

For this example, the DataFrame for Rivers Clothing depicts their inventory based on available sizes (index). Running this code will show the maximum (highest) indexes.

Code – Pandas Example

df_inv = pd.DataFrame({'Tops':   [22, 12,  19,   8, 23],
                       'Pants':  [5,    7,    17,  19, 12],
                       'Coats':  [11,  18,   1,   16,  3]},
                       index =  ['XS','S', 'M', 'L', 'XL'])

result = df_inv.idxmax(axis=0)
print(result)

• Line [1] creates a DataFrame from a dictionary of lists and saves it to df_inv.
• Line [2] retrieves the location(s) of the maximum indexes across the rows. This output saves to the result variable.
• Line [3] outputs the result to the terminal.

Output

For this example, a 5-day series of daytime highs record. This method returns the maximum temperature index.

Code – Series Example

temps = pd.Series(data=[5, 11, 24, 35, 49],
                  index=['Day-1', 'Day-2', 'Day-3', 'Day-4', 'Day-5'])
print(temps.idxmax())

• Line [1] creates a DataFrame from a dictionary of lists and saves it to df_inv.
• Line [2] retrieves the location(s) of the maximum indexes. This output is printed right away.

Output

Day-5

Note: The Numpy version of this method is numpy.argmax.

DataFrame idxmin()

The idxmin() method checks for and returns the index of the first occurrence of the minimum index(es) over a selected axis.

The syntax for this method is as follows:

DataFrame.idxmin(axis=0, skipna=True)

The DataFrame for Rivers Clothing depicts their inventory based on available sizes (indexes).

Running this code will show the minimum (lowest) indexes.

Code – Pandas Example

df_inv = pd.DataFrame({'Tops':   [22, 12,  19,   8, 23],
                       'Pants':  [5,  7,  17,  19, 12],
                       'Coats':  [11,  18,  1,  16,  3]},
                       index =   ['XS','S', 'M', 'L', 'XL'])

result = df_inv.idxmin(axis=0)
print(result)

• Line [1] creates a DataFrame from a dictionary of lists and saves it to df_inv.
• Line [2] retrieves the location(s) of the minimum indexes across each row. This output saves to the result variable.
• Line [3] outputs the result to the terminal.

Output

For this example, a 5-day series of daytime highs record. This method returns the minimum temperature index.

Code – Series Example

temps = pd.Series(data=[5, None, 24, 35, 49],
                  index=['Day-1', 'Day-2', 'Day-3', 'Day-4', 'Day-5'])
print(temps.idxmin())

• Line [1] creates a Series of temperatures and saves it to temps.
• Line [2] retrieves the location(s) of the maximum indexes across the rows and outputs the result to the terminal.

Output

Day-1

Note: The Numpy version of this method is numpy.argmin.

DataFrame reindex()

The reindex() method configures a DataFrame/Series to a new index. This method uses the parameter fill logic to replace the NaN/NULL values occurring from this operation.

The syntax for this method is as follows:

DataFrame.reindex(labels=None, index=None, columns=None, axis=None, method=None, 
                  copy=True, level=None, fill_value=nan, limit=None, tolerance=None)

The DataFrame reindex() method has two (2) calling conventions:

• (index=index_labels, columns=column_labels)
• (labels, axis={'index', 'columns'})

For this example, Rivers Clothing wants to replace XL with XXS. Running the code below accomplishes this task.

df_inv = pd.DataFrame({'Tops':   [22, 12,  19,   8, 23],
                       'Pants':  [5,    7,    17,  19, 12],
                       'Coats':  [11,  18,   1,   16,  3]},
                       index =   ['XS', 'S',  'M',  'L',  'XL'])

new_index = ['XXS', 'XS', 'S', 'M', 'L']
result = df_inv.reindex(new_index, fill_value=0)
print(result)

• Line [1] creates a DataFrame from a dictionary of lists and saves it to df_inv.
• Line [2] does the following:
  • Sets the new index for the DataFrame (adding in XXS and removing XL).
  • Fills the vacant values and replaces them with zeros (0).
  • Saves the output to result.
• Line [3] outputs the result to the terminal.

Output

DataFrame reindex_like()

The reindex_like() method returns an object (DataFrame/Series) with matching indexes as another object (DataFrame/Series).

💡 Note: A new object (DataFrame/Series) creates unless the new index is the same as the current one and the copy parameter is False.

For this example, the DataFrames (df1 & df2) contain a 4-day/3-day daily forecast of the daytime stats such as Celsius and Fahrenheit and Wind Speed.

df1 = pd.DataFrame([[24, 115, 'extreme'],
                    [31, 87,  'high'],
                    [22, 65,  'medium'],
                    [3,  9,   'low']],
                   columns=['Cel.', 'Fah.', 'Wind'],
                   index=pd.date_range(start='2014-02-12', end='2014-02-15', freq='D'))

df2 = pd.DataFrame([[8,  'low'],
                    [3,  'low'],
                    [54, 'medium']],
                   columns=['temp_celsius', 'windspeed'],
                   index=pd.DatetimeIndex(['2014-02-12', '2014-02-13', '2014-02-15']))

print(df1)
result = df2.reindex_like(df1)
print(result)

• Line [1] creates a DataFrame with Celsius, Fahrenheit, and Wind for four (4)  days, using date_range() and saves it to df1.
• Line [2] creates a DataFrame with Celsius, Fahrenheit, and Wind for three (3) days using DateTimeIndex() and saves it to df2.
• Line [3] outputs df1 to the terminal.
• Line [4] performs a reindex_like() on the DataFrames and saves it to the result variable.
• Line [5] outputs the result to the terminal.

Output

df1

df2

DataFrame rename()

The rename() method changes the axis label(s) in a DataFrame/Series.

The syntax for this method is as follows:

DataFrame.rename(mapper=None, index=None, columns=None, axis=None, copy=True, inplace=False, level=None, errors='ignore')

For this example, the same 4-day forecast DataFrame used above modifies.

df = pd.DataFrame([[24, 115, 'extreme'],
                   [31, 87,  'high'],
                   [22, 65,  'medium'],
                   [3,  9,   'low']],
                  columns=['Cel.', 'Fah.', 'Wind'],
                  index=pd.date_range(start='2014-02-12', end='2014-02-15', freq='D'))

result = df.rename(columns={"Cel.": "Celsius", "Fah.": "Fahrenheit"})
print(result)

• Line [1] creates a DataFrame with Celsius, Fahrenheit, and Wind for four (4) days, using date_range() and saves it to df1.
• Line [2] renames the columns to those set out in the columns parameter and saves it to the result variable.
• Line [3] outputs the result to the terminal.

Output

DataFrame rename_axis()

The rename_axis() method works the same as rename(): it sets the name of the axis for the index or columns.

The syntax for this method is as follows:

DataFrame.rename_axis(mapper=None, index=None, columns=None, axis=None, copy=True, inplace=False)

For this example, the same 4-day forecast DataFrame as above changes.

df = pd.DataFrame([[24, 115, 'extreme'],
                   [31, 87,  'high'],
                   [22, 65,  'medium'],
                   [3,  9,   'low']],
                  columns=['Cel.', 'Fah.', 'Wind'],
                  index=pd.date_range(start='2014-02-12', end='2014-02-15', freq='D'))

result = df.rename_axis("Dates")
print(result)

• Line [1] creates a DataFrame with Celsius, Fahrenheit, and Wind for four (4) days, using date_range() and saves it to df.
• Line [2] renames the index and saves it to the result variable.
• Line [3] outputs the result to the terminal.

Output