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Pandas for data science

pandas is like Excel in Python: it uses tables (namely DataFrame) and operates transformations on the data. But it can do a lot more. The Pandas DataFrame is a structure that contains two-dimensional data and its corresponding labels. DataFrames are widely used in data science, machine learning, scientific computing, and many other data-intensive fields.

DataFrames are similar to SQL tables or the spreadsheets that you work with in Excel or Calc. In many cases, DataFrames are faster, easier to use, and more powerful than tables or spreadsheets because they’re an integral part of the Python and NumPy ecosystems.

import pandas as pd

The most elementary functions of pandas:

  1. Reading data data = pd.read_csv(‘my_file.csv’)
  2. Writing data data.to_csv(‘my_new_file.csv’, index=None)
  3. Checking the data data.shape data.describe()
  4. Seeing the data : data.head(3) , data.loc[8]

for more functions

Analyzing Tabular Data using Python and Pandas

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What is Pandas? What makes it useful?

These Pandas features are comprehensive and tell you in detail about the aspects you should know before starting off with Pandas in the following pic

features

resources: