In this article, we are going to see how to utilize Pandas DataFrame and series for data wrangling.
The process of cleansing and integrating dirty and complicated data sets for easy access and analysis is known as data wrangling. As the amount of data raises continually and expands, it is becoming more important to organize vast amounts of data for analysis. Data wrangling comprises activities such as data sorting, data filtering, data reduction, data access, and data processing. Data wrangling is one of the most important tasks in data science and data analysis. Let’s see how to utilize Pandas DataFrame and series for data wrangling.
Used CSV: https://drive.google.com/file/d/1QK_0q4TjguwKjRMePk5NoQ29JcSfnGAe/view
Utilize series for data wrangling
Creating Series
pd.Series() method is used to create a pandas Series. In this, a list is given as an argument and we use the index parameter to set the index of the Series. The index helps us to retrieve data based on conditions.
Python3
# importing packagesimport pandas as pd# creating a seriespopulation_data = pd.Series([1440297825, 1382345085, 331341050, 274021604, 212821986], index=['China', 'India', 'United States', 'Indonesia', 'Brazil'])print(population_data) |
Output:
Filtering data- Retrieving insights based on conditions from the data
From the previous data, we retrieve data on two conditions, one is the population of India and another is countries that have a population of more than a billion.
Python3
print('population of india is : \' + str(population_data['India']))print('population greater than a billion :')print(population_data[population_data > 1000000000]) |
Output:
We can also use dictionaries to create Series in python. In this, we have to pass a Dictionary as an argument in the pd.Series() method.
Python3
population_data = pd.Series({'China': 1440297825, 'India': 1382345085, 'United States': 331341050, 'Indonesia': 274021604, 'Brazil': 212821986})print(population_data) |
Output:
Changing indices by altering the index of series
In pd.Series the index can be manipulated or altered by specifying a new index series.
Python3
population_data.index = ['CHINA','INDIA', 'US','INDONESIA', 'BRAZIL']print(population_data) |
Output:
Utilize Pandas Dataframe for data wrangling
Creating Dataframe using CSV
In this example, we will use a CSV file to print top n (5 by default) rows of a DataFrame or series using the Pandas.head() method.
Python3
# importing packagesimport pandas as pd# loading csv datapopulation_data = pd.read_csv('employees.csv')# setting the index of the dataframepopulation_data=population_data.set_index('First Name')# head of the dataframepopulation_data.head() |
Output:
Describing DataFrame
pd.Describe() method is used to get the summary statistics of the Dataframe.
Python3
# importing packagesimport pandas as pd# loading csv datapopulation_data = pd.read_csv('employees.csv')population_data.describe() |
Output:
Setting and Resetting the index of the Dataframe
pd.set_index is used for setting and resetting the index of the Dataframe. Whereas, pd.reset_index() reverts the Dataframe back to the normal state. Here, the name of the column is given as an argument.
Example 1: Resetting the index of the Dataframe in Start Date columns
Python3
# importing packagesimport pandas as pd# creating a pandas Dataframepopulation_data = pd.read_csv('employees.csv')# setting the index of the dataframepopulation_data = population_data.set_index('Start Date')pd.DataFrame(population_data) |
Output:
Example 2: Resetting the index of the Dataframe in First Name columns
Python3
# importing packagesimport pandas as pd# creating a pandas Dataframepopulation_data = pd.read_csv('employees.csv')# resetting the index of the dataframepopulation_data = population_data.reset_index()population_data = population_data.set_index('First Name')pd.DataFrame(population_data) |
Output:
Deleting a column from the DataFrames
The column ‘Salary’ is deleted from the DataFrames from our CSV file.
Python3
# importing packagesimport pandas as pd# loading csv datapopulation_data = pd.read_csv('employees.csv')# deleting columndel population_data['Salary'] pd.DataFrame(population_data.head()) |
Output:
Reshaping dataframe
df.Transpose() function is used to find the transpose of the given DataFrame.
Python3
# importing packagesimport pandas as pd# loading csv datapopulation_data = pd.read_csv('employees.csv')# setting the index of the dataframepopulation_data=population_data.set_index('First Name')# displaying a transpose of the dataframepd.DataFrame(population_data.transpose().head()) |
Output:
Sorting the Dataframe
df.sort_values() function is used to sort data. In this, the column name is passed as a parameter.
Python3
# importing packagesimport pandas as pd# loading csv datapopulation_data = pd.read_csv('employees.csv')# setting the index of the dataframepopulation_data=population_data.set_index('First Name')# sorting the Dataframe based on Density of population per km columnsorted_dataframe = population_data.sort_values('Salary', ascending=False)pd.DataFrame(sorted_dataframe) |
Output:
Dealing with missing values
Missing or null values can be checked with the Pandas df.null() method.
Python3
# importing packagesimport pandas as pd# loading csv datadata = pd.read_csv('employees.csv')# checking for null valuesdata.isnull().sum() |
Output:
Dropping Rows
We can filter rows that have null values by using df.dropna() method.
Python3
# importing packagesimport pandas as pd# loading csv datadata = pd.read_csv('employees.csv')# dropping NA valuesdata = data.dropna(axis=0, how='any')# checking for null valuesdata.isnull().sum() |
Output:
Grouping Data
In Data Analysis, the Grouping of data sets is a common requirement when the outcome must be expressed in terms of many groups. Panadas provides us with a built-in mechanism for grouping data into several categories. The pandas‘ df.groupby() technique is used for grouping data.
In the below code, We will create a DataFrames of students and their grades. In this groupby() method is used to group students according to their grades with their names.
Python3
# importing pandas as pdimport pandas as pd# Creating the dataframedf = pd.read_csv("employees.csv")# First grouping based on "Team"# Within each team we are grouping based on "Position"data = df.groupby(['First Name', 'Gender'])# Print the first value in each groupdata.first() |
Output:
Merging Dataframe
Pandas df.merge() method is used to merge two DataFrames. There are different ways of merging DataFrames like, outer join, inner join, left join, right join, etc.
Python3
import pandas as pd# reading two csv filesdata = pd.read_csv('employees.csv')# creating two dataframe head_data = data.head()tail_data = data.tail()# get top 5 rowsprint("Head Data :")display(head_data)# get last 5 rowsprint("Tail Data :")display(tail_data)# merge dataframemerge_data = pd.merge(head_data, tail_data, how='outer' )print("After merging: ")display(merge_data) |
Output:
Concatenating Data
The Concat function is used to conduct concatenation operations along an axis. Let’s create two DataFrames and concatenate them.
Python3
import pandas as pd# reading two csv filesdata1 = pd.read_csv('employees.csv')data2 = pd.read_csv('borrower.csv')# concatenating the dataframespd.DataFrame(pd.concat([data1,data2])) |
Output:
