In this tutorial, we will learn How to count occurrences for unique row values with Pandas which we can perform using a built in function value_counts() which helps in getting the total count and distinct for different values.
Pandas
As we know that pandas are a library used for manipulating and analyzing data in a data frame. There are some useful features which are there in it that we use for making required changes in our data frames.
- Data structures: It provides two main data structures, Series and DataFrame, that allow for efficient storage and manipulation of data. Which is nothing but providing a structure to the data we are storing in the memory
- Data cleaning: It provides a range of tools for cleaning and pre-processing data, including functions for handling missing or null values, filtering, and transforming data. Which involves the removal of unwanted data or incomplete values. It may also involve the removal of out liers.
- Data analysis: It provides a range of functions for data analysis, including descriptive statistics, group by operations, merging and joining datasets, and time series analysis.
- Data visualization: It provides integration with popular visualization libraries like Matplotlib and Seaborn to create visualizations of the data.
so for counting Occurrences for different row values. Here we have taken an example for the same where a pseudo code is given for finding the different values.
import pandas as pd
# Create a sample dataframe
df = pd.DataFrame({' fruit': ['apple', 'orange', 'banana', 'apple', 'orange', 'orange']})
# Count the occurrences of different row values
counts = df ['fruit']. value_ counts()
# Print the counts
print( counts)
In the above code first, we created the sample data frame with a column name fruit and then we run the value counts() method on the fruit column to get the total count of the occurrence of different values. The result of the count will be given the total count.
And for the result of the above code will be as given below:
orange 3 apple 2 banana 1 Name: fruit, dtype: int64
The count of each element is given here as the result.
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