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 pd import matplotlib.pyplot as plt # Make example dataframe df = 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 list su1 = df[ 'country' ].value_counts().index.tolist() print (su1) # print 1st unique country name present in a list su2 = 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 pd import matplotlib.pyplot as plt # Make example dataframe df = 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 counts su3 = df[ 'country' ].value_counts() print (su3) # print 1st country count in a list su4 = df[ 'country' ].value_counts()[ 0 ] print (su4) |
Output:
Example 3: To print our data using a loop from a list.
Python3
import pandas as pd import matplotlib.pyplot as plt # Make example dataframe df = 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 pd import matplotlib.pyplot as plt # Make example dataframe df = 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 Graph df[ 'country' ].value_counts().plot(kind = 'bar' ) plt.show() |
Output:
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