Pandas melt(), explode(), squeeze(), to_xarray(), transpose()

The Pandas DataFrame/Series has several methods to re-shape, sort, and transpose the data.

Preparation

Before any data manipulation can occur, two (2) new libraries will require installation.

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• The Pandas library enables access to/from a DataFrame.
• The Xarray library works with labeled multi-dimensional arrays and advanced analytics.

To install these libraries, 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.

$ pip install xarray

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

If the installations were 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
import xarray

DataFrame melt()

The melt() method unpivots a DataFrame/Series from the standard wide (horizontal) format to long (vertical).

The syntax for this method is as follows:

DataFrame.melt(id_vars=None, value_vars=None, var_name=None, 
               value_name='value', col_level=None, ignore_index=True)

For this example, four (4) students enroll in coding classes. The output will display in various formats using the melt() method.

Code – Example 1

df = pd.DataFrame({'Enrolled': {0: '01/01/2021', 1: '01/02/2021',  2: '01/29/2021', 3: '01/13/2021'},
                   'Student':  {0: 'Micah',      1: 'Philip',   2: 'Jonas',         3: 'Sarah'},
                   'Class':    {0: 'Python',     1: 'PHP',     2: 'JavaScript', 3: 'Java'},
                   'Grade':    {0: 65, 1: 76, 2: 55, 3: 81}})
print(df)

result = pd.melt(df, id_vars =['Student'], value_vars =['Class', 'Grade'])
print(result)

• Line [1] creates a DataFrame from a dictionary containing student details and saves it to df.
• Line [2] outputs the DataFrame to the terminal.
• Line [3] sets the id_vars to Student and the value_vars to Class and Grade. The output saves to result.
• Line [4] outputs the result to the terminal.

Output

df

result

💡 Note: From the code on line [3] and the result, the melt() method unpivots the DataFrame around the Student column (id_vars =['Student']).

For this example, the column labels (shown above) will update. Previously the default column labels were named variable and value (see example 1).

Below the column labels change.

Code – Example 2

df = pd.DataFrame({'Enrolled': {0: '01/01/2021', 1: '01/02/2021',  2: '01/29/2021', 3: '01/13/2021'},
                   'Student':  {0: 'Micah',      1: 'Philip',   2: 'Jonas',         3: 'Sarah'},
                   'Class':    {0: 'Python',     1: 'PHP',     2: 'JavaScript', 3: 'Java'},
                   'Grade':    {0: 65, 1: 76, 2: 55, 3: 81}})

result = pd.melt(df, id_vars =['Student'], value_vars =['Class', 'Grade'],
                 var_name ='All-Students', value_name ='All-Grades')
print(result)

• Line [1] creates a DataFrame from a dictionary containing student details and saves it to df.
• Line [2] sets the id_vars to Student and the value_vars to Class and Grade. This line also changes the default names variable and value to All-Students and All-Grades. The output saves to result.
• Line [3] outputs the result to the terminal.

result

DataFrame explode()

The explode() method reshapes (transforms) the DataFrame/Series elements from a list-like scenario to a row format.

The syntax for this method is as follows:

DataFrame.explode(column, ignore_index=False)

For this example, a public school conducts three (3) classes per day for three (3) grades (6-8).

Each grade contains a list with the number of students per class. For example, the Grade-7 class explodes to display each element inside the list in a long (vertical) format.

df = pd.DataFrame({'Grade-6': [[15, 11, 10]],
                   'Grade-7': [[12, 16, 11]],
                   'Grade-8': [[20, 18, 19]]})
print(df)

result = df.explode('Grade-7')
print(result)

• Line [1] creates a DataFrame from a Dictionary containing class details and saves it to df.
• Line [2] outputs the DataFrame to the terminal.
• Line [3] explodes the format for Grade-7. This output saves to result.
• Line [4] outputs the result to the terminal.

Output

df

result

DataFrame squeeze()

The squeeze() method compresses a one-dimensional DataFrame/Series axis into a Series.

💡 Note: Squeezing objects containing more than one element per axis does not change the original DataFrame/Series. This method is most effective when used with a DataFrame.

The syntax for this method is as follows:

DataFrame.squeeze(axis=None)

For this example, we have two (2) classical composers. Each composer contains a list with their total number of Preludes and Nocturnes. The DataFrame squeezes to display the details for Chopin.

Code – Example 1

df = pd.DataFrame([[24, 18], 
                   [4, 21]], 
                   columns=['Debussy', 'Chopin'])
print(df)

col = df[['Chopin']]
result = col.squeeze('columns')
print(result)

• Line [1] creates a DataFrame, assigns the column names, and saves it to df.
• Line [2] outputs the DataFrame to the terminal.
• Line [3] slices out the column containing Chopin’s composition details and saves it to col.
• Line [4] squeezes the column. The output saves to result.
• Line [5] outputs the result to the terminal.

Output

df

result

DataFrame to_xarray()

The to_xarray() method returns an xarray. Dataset/xarray.DataArray from a DataFrame/Series dependent on the object passed.

💡 Note: If the object is a DataFrame, the data converts to a Dataset.

If a Series, the data converts to a DataArray.

The syntax for this method is as follows:

DataFrame.to_xarray()

This method has no parameters.

For this example, Alice needs to grocery shop for a party. This DataFrame displays the grocery list (including categories, prices, and amounts). This object then converts to a Dataset.

Code – Example 1

df = pd.DataFrame([('Fruits',          'Apples',    3.97,   4),
                   ('Dairy',           'Milk',       2.43,   4),
                   ('Vegetables', 'Carrots',  4.21,  12),
                   ('Meat',           'Steak',    18.57, 4)],
                   columns=['Category', 'Name', 'Price', 'Quantity'])
print(df)

result = df.to_xarray()
print(result)

• Line [1] creates a DataFrame from a list of tuples containing grocery list details and saves it to df.
• Line [2] outputs the DataFrame to the terminal.
• Line [3] converts the DataFrame to a Dataset. The output saves to result.
• Line [4] outputs the result to the terminal.

Output

df

result

Dimensions:   	(index: 4)
Coordinates:
  * index     	(index) int64 0 1 2 3
Data variables:
    Category	(index) object 'Fruits' 'Dairy' 'Vegetables' 'Meat'
    Name      	(index) object 'Apples' 'Milk' 'Carrots' 'Steak'
    Price     		(index) float64 3.97 2.43 4.21 18.57
    Quantity		(index) int64 4 4 12 4

You could also extract one (1) column from a DataFrame.

Code  – Example 2

df = pd.DataFrame([('Fruits', 'Apples', 3.97, 4),
                   ('Dairy', 'Milk', 2.43, 4),
                   ('Vegetables', 'Carrots', 4.21, 12),
                   ('Meat', 'Steak', 18.57, 4)],
                   columns=['Category', 'Name', 'Price', 'Quantity'])

result = df['Name'].to_xarray()
print(result)

• Line [1] creates a DataFrame from a List of Tuples containing grocery list details and saves it to df.
• Line [2] converts a column of the DataFrame to a Dataset. The output saves to result.
• Line [3] outputs the result to the terminal.

Output

<xarray.DataArray 'Name' (index: 4)>
array(['Apples', 'Milk', 'Carrots', 'Steak'], dtype=object)
Coordinates:
  * index    (index) int64 0 1 2 3

This example converts a Series containing the first six (6) months of the year and the days per month to an xarray.

Code – Example 3

my_series = pd.Series(['Jan.', 'Feb.', 'Mar.', 'Apr.', 'May', 'June'])
my_idx    = [31, 28, 31, 30, 31, 30]
my_series.index = my_idx
print(my_series)

result = my_series.to_xarray()
print(result)

• Line [1] creates a Series from a list of months.
• Line [2] creates and sets the indexes as a list of days for each month.
• Line [3] sets the index for the Series.
• Line [4] outputs the Series to the terminal.
• Line [5] converts the Series to an xarray.
• Line [6] outputs the result to the terminal.

Output

my_series

result

dtype: object
<xarray.DataArray (index: 6)>
array(['Jan.', 'Feb.', 'Mar.', 'Apr.', 'May', 'June'], dtype=object)
Coordinates:
  * index    (index) int64 31 28 31 30 31 30

DataFrame T & transpose()

The T or transpose() method switches (transposes) the index and columns.

The syntax for this method is as follows:

DataFrame.transpose(*args, copy=False)

For this example, the countries.csv file reads in.

💡 Note: Click here to download the CSV file. Move to the current working directory.

df = pd.read_csv('countries.csv').head(3)
print(df)

result1 = df.T
print(result1)

result2 = df.transpose()
print(result2)

• Line [1] reads in the top three (3) rows of the comma-delimited CSV file. The output saves to df.
• Line [2] outputs the DataFrame to the terminal.
• Line [3] uses the T method to transpose the DataFrame. The output saves to result1.
• Line [4] outputs result1 to the terminal.
• Line [5] uses the transpose() method to transpose the DataFrame. The output saves to result2.
• Line [6] outputs result2 to the terminal.

Output

df

result1

result2

💡 Note: The output from result1 and result2 are identical.

Further Learning Resources

This is Part 15 of the DataFrame method 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 the serialization and conversion methods from_dict(), to_dict(), from_records(), to_records(), to_json(), and to_pickles().
• Part 22 focuses on the serialization and conversion methods to_clipboard(), to_html(), to_sql(), to_csv(), and to_excel().
• Part 23 focuses on the serialization and conversion methods to_markdown(), to_stata(), to_hdf(), to_latex(), to_xml().
• Part 24 focuses on the serialization and conversion methods to_parquet(), to_feather(), to_string(), Styler.
• Part 25 focuses on the serialization and conversion methods to_bgq() and to_coo().

Also, have a look at the Pandas DataFrame methods cheat sheet!