­­Handling Missing Data in Pandas: backfill(), bfill(), fillna(), dropna(), interpolate()

The Pandas DataFrame/Series has several methods to handle Missing Data. When applied to a DataFrame/Series, these methods evaluate and modify the missing elements.

Preparation

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

• The Pandas library enables access to/from a DataFrame.
• The NumPy library supports multi-dimensional arrays and matrices in addition to a collection of mathematical functions.

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 numpy

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 libraries.

• How to Install Pandas on PyCharm
• How to Install NumPy 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 numpy as np 

DataFrame backfill() and bfill()

The DataFrame backfill() and bfill() methods backward fill missing data (such as np.nan, None, NaN, and NaT values) from the DataFrame/Series.

The syntax for these methods is as follows:

DataFrame.backfill(axis=None, inplace=False, limit=None, downcast=None)
DataFrame.bfill(axis=None, inplace=False, limit=None, downcast=None)

Throughout this article, we use the same DataFrame example. This DataFrame contains three (3) rows with missing data. Each example attempts to handle the missing data.

In this example, the DataFrame contains some missing data. This code will attempt to (replace) these values using the bfill() method.

Code – Example 1

df = pd.DataFrame({'Data-1':  [None, 11, 12], 
                   'Data-2':  [13, 14, None],
                   'Data-3':  [None, 15, 16]})
print(df)

result = df.bfill(axis='rows')
print(result)

• Line [1] creates a dictionary of lists and saves it to df.
• Line [2] outputs the DataFrame to the terminal. The missing values convert to NaN.
• Line [3] backfills the NaN values across the rows. This output saves to the result variable.
• Line [4] outputs the result to the terminal.

Output

df

result

💡 Note: backfill/bfill tries to fill in the NaN values with data from the same position in the next row. If there is no next row or the next row contains NaN, the value does not change.

Code – Example 2

df = pd.DataFrame({'Data-1':  [None, 11, 12], 
                   'Data-2':  [13, 14, 'NaN'],
                   'Data-3':  [None, 15, 16]})
print(df)

result = df.bfill(axis='rows')
print(result)

• Line [1] creates a dictionary of lists and saves it to df.
• Line [2] outputs the DataFrame to the terminal. The missing values convert to NaN.
• Line [3] backfills the NaN values across the rows. This output saves to the result variable.
• Line [4] outputs the result to the terminal.

Output

df

result

💡Note: The output is identical to that in Example 1.

Code – Example 3

df = pd.DataFrame({'Data-1':  [None, 11, 12], 
                   'Data-2':  [13, 14, 'NaN'],
                   'Data-3':  [None, 15, 16]})
print(df)

result = df.bfill(axis='rows', downcast='infer')
print(result)

• Line [1] creates a dictionary of lists and saves it to df.
• Line [2] outputs the DataFrame to the terminal. All missing values convert to NaN.
• Line [3] backfills the NaN values across the rows. The infer parameter attempts to change the dtype across the DataFrame/Series. This output saves to the result variable.
• Line [4] outputs the result to the terminal.

Output

df

result

DataFrame fillna()

The fillna() method fills in the DataFrame/Series missing data (NaN/None) with the content of the value parameter is shown below.

The syntax for this method is as follows:

Frame.fillna(value=None, method=None, axis=None, 
             inplace=False, limit=None, downcast=None)

In this example, the DataFrame contains some missing data. This code will attempt to (replace) these values using the fillna() method.

df = pd.DataFrame({'Data-1':  [np.nan, 11, 12], 
                   'Data-2':  [13, 14, np.nan],
                   'Data-3':  [np.nan, 15, 16]},
                   index=['Row-1', 'Row-2', 'Row-3'])
print(df)

result = df.fillna(22, downcast='infer')
print(result)

• Line [1] creates a dictionary of lists and saves it to df.
• Line [2] outputs the DataFrame to the terminal. All np.nan values convert to NaN.
• Line [3] fills in the missing values across the rows with the value 22. The infer parameter attempts to change the dtype across the DataFrame/Series. This output saves to the result variable.
• Line [4] outputs the result to the terminal.

Output

df

result

💡 Note: The output using ffill() is the same as if you use fillna() and pass the method parameter as ffill.

DataFrame dropna()

The dropna() method removes missing data from a DataFrame/Series.

The syntax for this method is as follows:

DataFrame.dropna(axis=0, how='any', thresh=None, subset=None, inplace=False)

Note: A list of a few possible empty values are:

• 'NaN'
• pd.NaN
• np.nan
• None
• NaT

In this example, the DataFrame contains some missing data. Therefore, this code will attempt to remove the rows that contain these values.

df = pd.DataFrame({'Data-1':  [np.nan, 11, 12], 
                   'Data-2':  [13, 14, pd.NaT],
                   'Data-3':  [None, 15, 16]},
                   index=['Row-1', 'Row-2', 'Row-3'])
print(df)

result = df.dropna()
print(result)

• Line [1] creates a dictionary of lists and saves it to df.
• Line [2] outputs the DataFrame to the terminal.
• Line [3] removes the rows containing missing values. This output saves to the result variable.
• Line [4] outputs the result to the terminal.

Output

df

result

💡 Note: Row-2 is the only row that contains valid data and the only row left after applying the dropna() method.

DataFrame interpolate()

The interpolate() method fills all NaN values using interpolation.

The syntax for this method is as follows:

DataFrame.interpolate(method='linear', axis=0, limit=None, inplace=False, limit_direction=None, limit_area=None, downcast=None, **kwargs)

The DataFrame in this example contains missing data. This code will attempt to replace these values.

df = pd.DataFrame({'Data-1':  [np.nan, 11, 12], 
                   'Data-2':  [13, 14, pd.NaT],
                   'Data-3':  [None, 15, 16]},
                   index=['Row-1', 'Row-2', 'Row-3'])
print(df)

result = df.interpolate(method='linear', limit_direction='backward', axis=0)
print(result)

• Line [1] creates a dictionary of lists and saves it to df.
• Line [2] outputs the DataFrame to the terminal.
• Line [3] interpolates and sets the parameters to linear, the direction to backward, and the axis to zero (0). This output saves to the result variable.
• Line [4] outputs the result to the terminal.

Output

df

result

Further Learning Resources

This is Part 11 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!