Pandas 🐼 Python Data Analysis Library

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

import pandas as pd

Column | Feature | Attribute | Series | Field | Dimension

Row | Index | Record | Tuple | Observation | Sample

• 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.

Features of Pandas:

• Time series support
• Handling missing values
• Grouped operations
• Categorical data support
• Merging and joining DataFrames
• Statistical functions
• Data visualization tools

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 : 1D Array

• 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

DataFrame: 2D Array

• 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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