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
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)
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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.
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)
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Output:
Example 3: To print our data using a loop from a list.
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])
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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.
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: