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What are the advantages of using the python pandas library?
Firstly we can say that It has Various tools to support data load into data objects(pandas DataFrame and Series) irrespective of their file formats. This means we can read tabular data which is any file format by using any of the pandas input functions. List of some pandas input functions are read_table, read_csv, read_html, read_excel, read_json, read_orc, read_sql, and many more.
Example
df = pd.read_table('file.txt',sep=' ')
dfExplanation
In the above example, we have a text file with tabular data, and the data is separated by space (between each column). Here we created a DataFrame by using this read_table method and keyword argument sep. The input for keyword argument sep is space(“ “) because the data in this text file is separated by spaces only.
Output
column1 column2 0 1 2 1 3 4 2 5 6
The above output is the data stored in pandas data object (DataFrame object ) df and it is tabular data from our text file “file.txt”.
In the same way, we have various features available in this pandas library like
We can Customize the table index of our indexed DataFrame objects.
We can reshape the data in our DataFrame object to get more information from the data table. To reshape a DataFrame we use many methods like pivot, melt, and more.
We can slice the data by using pandas data objects label-oriented slicing techniques. Can be done by ranging, loc, and iloc methods.
Example
Series[:2]
Explanation
In this above example, we get a sliced set of pandas Series object, here we have done this by using slice ranging to our pandas data object (Series object).
And we can Merge high-performance datasets(multiple DataFrame) efficiently.
It supports Time series-functionality so that we can work with data that is related to date and time.
The pandas package contains multiple methods for convenient data filtering operations.
In the same way, we have more features available in this pandas package to work with any form of data in python.
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