`Pandas` 🐼 Python Data Analysis Library

Python library that helps structure data in DataFrames and contains built-in data analysis functions.

import pandas as pd

Pandas is an exploratory data analysis toolkit with a rich set of attributes and methods
Pandas provide a wide range of functions and methods
Widely used for data cleaning, data exploration, data manipulation, and data analysis tasks.
Toolkit for reading, writing, accessing, filtering, grouping, aggregating, merging, joining, combining, reshaping, cleaning, selecting data and performing statistical computation. The financial term for multidimensional structured data sets is Panel
Supports various formats of data: csv, tsv, txt, xls, xlsx, json, etc.
Performance optimization ( Changing data types, storage type )
Integrates well with other important libraries like NumPy, Matplotlib, Seaborn, Scipy, etc.

Data Type or Data Structure	Description
`pandas.Series()`	`1D array` is an object that can hold any data type.
`pandas.DataFrame()`	`2D table` is like a data structure that can hold multiple types of data in columns.

Attribute	Meaning
`df.index`	The row index labels of DataFrame ( Default: RangeIndex
`df.columns`	The column index labels of DataFrame (axis = 1)
`df.size`	Number of columns in DataFrame
`df.shape`	A tuple of rows and columns ( nrows, ncols )
`df.ndim`	Number of dimensions in the DataFrame ( 1D, 2D, 3D )
`df.values`	Values of DataFrame
`df.axes`	List containing index and columns indices in a DataFrame

Method	Use
`pd.read_csv()`, `pd.read_excel()`, `pd.read_json()`	Import data
`df.to_csv()`, `df.to_excel()`, `df.to_parquet()`	Export data
`df.head()`, `df.tail()`, `df.sample()`,`df.sort_values()`	Preview data
`df.query()`	Filter data
`df.iat[]`, `df.at[]`, `df.iloc[]`, `df.loc[]`	Indexing and Slicing
`df.info()`	Metadata Information
`df.dropna()`, `df.fillna()`, `df.drop_duplicates()`, `df.rename()`, `df.set_index()`	Clean data
`df.apply()`, `df.map()`, `df.reduce()`, `df.explode()`	Transform data
`df.groupby()`, `df.groupby().agg()`, `df.groupby().aggregate()`	Group and aggregate data
`df.join()`, `df.merge()`, `df.concat()`	Combine data
`df.pivot_table()`, `df.stack()`, `df.unstack()`	Reshape data
`df.plot()`	Visualize data
`df.sum()`, `df.mean()`, `df.median()`, `df.max()`, `df.value_counts()`, `df.describe()`	Mathematical operations
`df.date_range()`, `df.to_datetime()`	Time Series analysis

Series holds homogeneous data values, i.e. All data values are of same data type.
Data axis labels are called as index

# Create a series:
pd.Series([1, 2, 3, 4])

# Accessing a series:
DataFrame['SeriesName'] or DataFrame.SeriesName

Data is aligned in tabular form with rows and columns
DataFrame is a sequence of Series that shares the same index
The Python equivalent of an Excel or SQL table which is used to store and analyze data.

# Empty DataFrame:
pd.DataFrame()

# Accessing DataFrame:
DataFrame[['SeriesName1', 'SeriesName2', 'SeriesName3']]

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