Pandas provides various data structures and operations for manipulating numerical data and time series. However, there can be cases where some data might be missing. In Pandas missing data is represented by two value:
- None: None is a Python singleton object that is often used for missing data in Python code.
- NaN: NaN (an acronym for Not a Number), is a special floating-point value recognized by all systems that use the standard IEEE floating-point representation
Pandas treat None and NaN as essentially interchangeable for indicating missing or null values. In order to drop a null values from a dataframe, we used dropna() function this function drop Rows/Columns of datasets with Null values in different ways.
Syntax:
DataFrame.dropna(axis=0, how=’any’, thresh=None, subset=None, inplace=False)Parameters:
axis: axis takes int or string value for rows/columns. Input can be 0 or 1 for Integer and ‘index’ or ‘columns’ for String.
how: how takes string value of two kinds only (‘any’ or ‘all’). ‘any’ drops the row/column if ANY value is Null and ‘all’ drops only if ALL values are null.
thresh: thresh takes integer value which tells minimum amount of na values to drop.
subset: It’s an array which limits the dropping process to passed rows/columns through list.
inplace: It is a boolean which makes the changes in data frame itself if True.
Code #1: Dropping rows with at least 1 null value.
# importing pandas as pdimport pandas as pd # importing numpy as npimport numpy as np # dictionary of listsdict = {'First Score':[100, 90, np.nan, 95], 'Second Score': [30, np.nan, 45, 56], 'Third Score':[52, 40, 80, 98], 'Fourth Score':[np.nan, np.nan, np.nan, 65]} # creating a dataframe from dictionarydf = pd.DataFrame(dict) df |
Now we drop rows with at least one Nan value (Null value)
# importing pandas as pdimport pandas as pd # importing numpy as npimport numpy as np # dictionary of listsdict = {'First Score':[100, 90, np.nan, 95], 'Second Score': [30, np.nan, 45, 56], 'Third Score':[52, 40, 80, 98], 'Fourth Score':[np.nan, np.nan, np.nan, 65]} # creating a dataframe from dictionarydf = pd.DataFrame(dict) # using dropna() function df.dropna() |
Output:
Code #2: Dropping rows if all values in that row are missing.
# importing pandas as pdimport pandas as pd # importing numpy as npimport numpy as np # dictionary of listsdict = {'First Score':[100, np.nan, np.nan, 95], 'Second Score': [30, np.nan, 45, 56], 'Third Score':[52, np.nan, 80, 98], 'Fourth Score':[np.nan, np.nan, np.nan, 65]} # creating a dataframe from dictionarydf = pd.DataFrame(dict) df |
Now we drop a rows whose all data is missing or contain null values(NaN)
# importing pandas as pdimport pandas as pd # importing numpy as npimport numpy as np # dictionary of listsdict = {'First Score':[100, np.nan, np.nan, 95], 'Second Score': [30, np.nan, 45, 56], 'Third Score':[52, np.nan, 80, 98], 'Fourth Score':[np.nan, np.nan, np.nan, 65]} df = pd.DataFrame(dict) # using dropna() function df.dropna(how = 'all') |
Output:
Code #3: Dropping columns with at least 1 null value.
# importing pandas as pdimport pandas as pd # importing numpy as npimport numpy as np # dictionary of listsdict = {'First Score':[100, np.nan, np.nan, 95], 'Second Score': [30, np.nan, 45, 56], 'Third Score':[52, np.nan, 80, 98], 'Fourth Score':[60, 67, 68, 65]} # creating a dataframe from dictionary df = pd.DataFrame(dict) df |
Now we drop a columns which have at least 1 missing values
# importing pandas as pdimport pandas as pd # importing numpy as npimport numpy as np # dictionary of listsdict = {'First Score':[100, np.nan, np.nan, 95], 'Second Score': [30, np.nan, 45, 56], 'Third Score':[52, np.nan, 80, 98], 'Fourth Score':[60, 67, 68, 65]} # creating a dataframe from dictionary df = pd.DataFrame(dict) # using dropna() function df.dropna(axis = 1) |
Output :
Code #4: Dropping Rows with at least 1 null value in CSV file.
Note: In this, we are using CSV file, to download the CSV file used, Click Here.
# importing pandas module import pandas as pd # making data frame from csv file data = pd.read_csv("employees.csv") # making new data frame with dropped NA values new_data = data.dropna(axis = 0, how ='any') new_data |
Output:
Now we compare sizes of data frames so that we can come to know how many rows had at least 1 Null value
print("Old data frame length:", len(data))print("New data frame length:", len(new_data)) print("Number of rows with at least 1 NA value: ", (len(data)-len(new_data))) |
Output :
Old data frame length: 1000 New data frame length: 764 Number of rows with at least 1 NA value: 236
Since the difference is 236, there were 236 rows which had at least 1 Null value in any column.
