Pandas Analysis – DataFrames
The purpose of this article is to introduce some of the key features of Pandas Analysis and illustrate how they can be used to analyze data in a variety of ways.
Pandas provides two main data structures for storing and manipulating data:
A Series is a one-dimensional array-like object that can store any data type, such as integers, strings, or even other Python objects. Each element in a Series has a label, which is called an index.
When we talk about Pandas Analysis, DataFrames are two-dimensional table-like data structure with columns of different data types. Each column in a DataFrame is a Series, and each row represents a record or an observation.
Pandas Data Analysis
Among the Pandas Analyzing methods, the head() method is one of the most commonly implemented methods for providing a fast overview of a DataFrame in regards to Pandas Analyzing.
Beginning at the top of the list, the head() method displays the headers and the given number of rows.
For our examples, we will be working with a file known as ‘language_data.csv‘ which is a CSV file.
Display the first row by utilizing the head() method of Pandas:
From the language_data.csv file retrieve the first eight rows of the DataFrame:
Utilizing the DataFrame, generate the top five rows:
Set the index name to “RANKING”:
It is also possible to view the last rows of the DataFrame using the tail() method of the DataFrame object.
In the tail() method, beginning at the bottom, the headers and a given number of rows are displayed, beginning at the top.
Utilizing the DataFrame, display the bottom five rows:
Apply the tail() method with the set_index() method:
DataFrames have a method known as info(), which you can access when you are analyzing the data set with Pandas, which provides you with additional information about the data set.
You can display the following information of language_data.csv file data as follows:
According to the outcome of Pandas Analyzing, there are 10 rows, and 3 columns in the dataset:
RangeIndex: 10 entries, 0 to 9
Data columns (total 3 columns):
Here is the title of every column, along with the data type:
# Column Non-Null Count Dtype
— –-–——- ——————- –-–—
0 RANKING 10 non-null int64
1 LANGUAGE 10 non-null object
2 USE 10 non-null object
First Implement the info() method then, shows the last two rows data:
The code above imports the pandas library as pds, reads in a CSV file called language_data.csv and assigns the resulting pandas DataFrame to the variable mrx_df.
The print(mrx_df.info()) statement then prints out summary information about the DataFrame, including the number of rows and columns, the names and data types of each column, and the number of non-null values in each column.
The print(mrx_df.tail(2)) statement prints out the last 2 rows of the DataFrame.
According to the info() method, we can also discover how many non-null values are found in each column, and based on our data set, it looks like that 10 non-null values are found in all columns of the language_data file in Pandas Analysis.
As a result, it indicates that there are no rows in the three columns of language_data,csv file that have no values for whatever reason.
An empty value, also known as a null value, can cause problems with regard to analyzing data.
You should also take into account eliminating rows with empty values.
Essentially, this is the first step in what is known as the process of cleaning data.