Duration: Hours

Pandas is an open-source data analysis and data manipulation library written in python. Pandas refer to “Panel Data”, which means a structured dataset. One of the most common uses for Python is in its ability to create and manage data structures.

Training Mode: Online


Data Manipulation in Python Objectives: 

a). Visualise data using methods from histograms to dimensionality reduction.

b). Create, save and serialise data frames in and out of multiple formats.

c). Clean and format data easily.

d). Detect and intelligently fill missing values.

e). Group, aggregate and summarise your data.

f). Merge data sources into a beautiful whole.

g). Pivot and cross-tabulate data like a pro.

h). Intersplice, summarise and investigate time series data.

i). Seamlessly work with data from different time zones.

1. Introduction to Data Manipulation

2. Dataset Basics

a). Finding Datasets

b). Jupyter Notebooks and Loading Data

c). Pandas vs Numpy

d). Creating DataFrames

e). Saving and Serialising

f). Inspecting DataFrames

3. Visual Exploration

a). Introduction and super basic plots

b). Pandas vs Matplotlib

c). Visualising 1D distributions

d). Visualising 2D distributions

e). Styling Pandas Table outputs

f). Higher dimension visualisations

4. Basic Data Manipulation

a). Introduction, Labelling and Ordering

b). Slicing and Filtering

c). Replacing and Thresholding

d). Removing and adding data

e). Apply, map and vectorised functions

5. Grouping of Pandas

a). Introduction and motivation

b). Basic grouping syntax

c). Intelligent imputation

d). Grouping aggregation

6. Merging with Pandas

a). Introduction and basic syntax

b). Different types of merging

c). Helpful merging functions

7. Advanced Manipulation – MultiIndex, Pivoting and more 

a). Introduction and basic MultiIndexes

b). MultiIndex II – MultiIndex Strikes Back

c). Stacking and Unstacking

d). Pivoting

e). Pivot Margins

f). Crosstab

g). Melting

8. Time series Data

a). Introduction and the Datetime Index

b). Reindexing

c). Resampling

d). Rolling functions


For more inputs on Data Manipulation in Python : a Pandas crash course you can connect here.
Contact the L&D Specialist at Locus IT.


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