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What is Pandas in python?
Pandas
Pandas is one of the powerful open source libraries in the Python programming language used for data analysis and data manipulation. If you want to work with any tabular data, such as data from a database or any other forms (Like CSV, JSON, Excel, etc.,) then pandas is the best tool.
History
In 2008, developer Wes McKinney started developing pandas for high-performance, flexible data analysis.
Highlight features
Pandas will reduce the complexity and make our work easy, and it can be applicable to any type of data that is ordered and unordered. The output of the pandas is also a tabular form named DataFrame.
We can plot some Visualization graphs by using Matplotlib which is also a python library, it provides different plotting types such as scatter, bar, boxplot,. . .
Example
Let’s see an example.
df = pd.DataFrame({'x': [3, 6, 8, 7, 12, 5, 10, 18],'y': [2, 5, 12, 3, 8, 4, 15, 6]})
df.plot()Output
In the above code and output graph, we have seen a simple basic Visualization plot for our data.
Filtering the data from a specific column or row, selecting, slicing, and extracting can be very flexible in pandas, there numerous methods are available in pandas to perform all these operations.
We can combine or concatenate multiple tables in pandas both column-wise and row-wise, also we can perform join/merge operations on data tables.
Time series is also one of the features of pandas, if you want to work with time and dates pandas will give you great support.
To perform mathematical operations, there is no need to use looping over all rows of our data table. Those operations can be done very easily by using some direct methods in pandas, shown in the below Example.
Example
Example : pandas.DataFrame.mean()
In the above code block pandas representing the name of the package, DataFrame representing the tabular data, and the mean() method will give you the mean value of each column data.
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