Prerequisites:
In this article, we will learn how we can extract the names and values using values_count() from panda. The panda library is equipped with a number of useful functions for ‘value_counts’ is one of them. This function returns the counts of unique items in a pandas data frame.
Syntax:
<object>.value_count()
Approach:
- Import Required module.
- Make the DataFrame
- Process using value_count()
- Display data
Example 1: To print all the unique country and the first country name in the list.
tolist() function return a list of the values.
Syntax: Index.tolist()
Parameters : None
Returns : list
Python3
import pandas as pdimport matplotlib.pyplot as plt # Make example dataframedf = pd.DataFrame([(1, 'Germany'), (2, 'France'), (3, 'Indonesia'), (4, 'France'), (5, 'France'), (6, 'Germany'), (7, 'UK'), (8, 'India'), (9, 'India'), (10, 'Germany') ], columns=['groupid', 'country'], index=['a', 'b', 'c', 'd', 'e', 'f', 'g', 'h', 'i', 'j']) # print all unique country name in the listsu1 = df['country'].value_counts().index.tolist()print(su1) # print 1st unique country name present in a listsu2 = df['country'].value_counts().index.tolist()[0]print(su2) |
Output:
Example 2: To print all the unique values of the column and the first value of the column.
value_count() counts Unique Occurrences of Values in a Column
Syntax: Index.value_count()
Parameters: None
Returns: the count of occurrences of each of the unique values in this column.
Python3
import pandas as pdimport matplotlib.pyplot as plt # Make example dataframedf = pd.DataFrame([(1, 'Germany'), (2, 'France'), (3, 'Indonesia'), (4, 'France'), (5, 'France'), (6, 'Germany'), (7, 'UK'), (8, 'India'), (9, 'India'), (10, 'Germany') ], columns=['groupid', 'country'], index=['a', 'b', 'c', 'd', 'e', 'f', 'g', 'h', 'i', 'j']) # print country name and countssu3 = df['country'].value_counts()print(su3) # print 1st country count in a listsu4 = df['country'].value_counts()[0]print(su4) |
Output:
Example 3: To print our data using a loop from a list.
Python3
import pandas as pdimport matplotlib.pyplot as plt # Make example dataframedf = pd.DataFrame([(1, 'Germany'), (2, 'France'), (3, 'Indonesia'), (4, 'France'), (5, 'France'), (6, 'Germany'), (7, 'UK'), (8, 'India'), (9, 'India'), (10, 'Germany') ], columns=['groupid', 'country'], index=['a', 'b', 'c', 'd', 'e', 'f', 'g', 'h', 'i', 'j']) # printing names and count using loop.for idx, name in enumerate(df['country'].value_counts().index.tolist()): print('Name :', name) print('Counts :', df['country'].value_counts()[idx]) |
Output:
Example 4: To print our data in the form of Bar graph.
Syntax: matplotlib.pyplot.plot(kind=’ ‘)
Parameters: kind: type of graph, i.e. line, bar.
Returns: This returns a Graph.
Python3
import pandas as pdimport matplotlib.pyplot as plt # Make example dataframedf = pd.DataFrame([(1, 'Germany'), (2, 'France'), (3, 'Indonesia'), (4, 'France'), (5, 'France'), (6, 'Germany'), (7, 'UK'), (8, 'India'), (9, 'India'), (10, 'Germany') ], columns=['groupid', 'country'], index=['a', 'b', 'c', 'd', 'e', 'f', 'g', 'h', 'i', 'j']) # Display data in a form of Graphdf['country'].value_counts().plot(kind='bar')plt.show() |
Output:
