Pandas library for data science (All in One)

Name: Pandas library for data science (All in One)
Rating: 4.4 (61 reviews)

learn pandas and it's functions by working on a dataset and by making your own dataframe

Created byShambhavi Gupta

Last updated 5/2022

English

English [Auto],

What you'll learn

Learn methods and attributes across numerous pandas functions
Perform the functions of data operations in Python's popular "pandas" library including filling null values, performing statistical functions and much more!
Defining your own datasets using pandas and numpy library
Learn functions important for data manipulation
Learn and master the most important Pandas functions
Bring your Data Handling & Data Analysis skills to an outstanding level.
Update your resume with one of the in demand skill : Data analysis Pandas
Detect and intelligently fill missing values.

Course content

5 sections • 21 lectures • 3h 58m total length

IDE online and offline10:33
Explore online and offline integrated development environments for Python in data science, using Google Cloud notebooks that auto-save to Drive and tools like PyCharm or VS Code for local setup.
What is pandas library and installation6:41
discover how the pandas library enables data manipulation and analysis in Python, create and apply functions to data frames, and install pandas on online or offline IDEs like Google Colab.
Reading a csv file8:50
Import pandas as pd; read a csv into a dataframe with pd.read_csv, assign to a variable, and preview with head and tail to explore sample data in Colab.

describe/shape and checking null values9:50
Explore Pandas data analysis with describe and shape, identify data types from numerical and categorical columns, and check for null values and memory usage using info and isnull.
Filling null values11:07
Learn how to handle missing data in pandas by filling, dropping, replacing, and interpolating null values with practical examples on a sample dataset.
replace function8:44
Learn how the pandas replace function swaps values in a data frame, including single and multiple value replacements, in-place edits, and handling missing values or characters and numbers.
interpolate function9:56
Explore how to create a data frame in pandas and apply the interpolate function to fill missing values, using forward and backward directions.

query function16:03
Learn how to create a pandas data frame, generate random data, group and label columns, and apply the query function to filter data with conditions.
insert and cumsum function14:10
Insert a new column at a chosen position, compute a cumulative sum for a column, and draw random samples using pandas functions.
isin, loc and iloc, rank function11:25
Explore how to filter data with isin, select data by label with loc and by position with iloc, and assign ranks using the rank function in pandas.

mean function10:55
Explore how to use the mean function in Pandas data frame to compute column means, and apply mean to rows using df.apply with a lambda.
covariance function12:35
Learn how to compute covariance between numerical columns in pandas, revealing the relationship between two variables and interpreting correlation using practical data examples.
correlation function11:42
Learn to compute the correlation between two numerical columns in a data frame, select columns, and see how results change, while non-numeric columns are automatically excluded.
.rolling function12:15
Explore the Pandas rolling function in data science, applying a moving window to a Series or DataFrame, specifying window size, and computing statistics like median or standard deviation.
.expanding function11:04
Learn to use the dot expanding function in pandas on a data series, including importing libraries, creating a dataset with an index and five columns, and applying the mean statistic.
.melt function11:49
Learn how to use the Pandas melt function to reshape a data frame from wide to long by selecting columns and assigning a name to the melted value column.

pivot function10:36
nunique function13:14
Master the pandas nunique function to count unique entries across rows or columns, analyze categorical features in a dataframe, and observe how to apply it in practice.
memory usage function13:29
Discover how the memory usage function reveals how much memory each dataframe column consumes, returns byte values, and can convert results to megabytes for large data frames.
select datatypes11:21
Learn how to use pandas select datatypes for condition-based subsetting of dataframe columns and how to apply replace to swap values, with practical examples.
nsmallest and nlargest function11:43
Explore how to use the nsmallest and nlargest functions to find the smallest and largest values in a data frame, and control the number of results.

Requirements

Should have some basic knowledge of python programming language
An internet connection capable of streaming videos.

Description

Data scientists spend only 20 percent of their time on building machine learning algorithms and 80 percent of their time finding, cleaning, and reorganizing huge amounts of data. That mostly happen because many use graphical tools such as Excel to process their data. However, if you use a programming language such as Python you can drastically reduce the time it takes for processing your data and make them ready for use in your project. This course will show how Python can be used to manage, clean, and organize huge amounts of data.

By the end of this course, you will be able to apply all majority of Data analysis function on various different datasets with built in function available in pandas

Why this course?

Data scientist is one of the hottest skill of 21st century and many organization are switching their project from Excel to Pandas the advanced Data analysis tool .

This course is basically design to get you started with Pandas library at beginner level, covering majority of important concepts of data processing data analysis and a Pandas library and make you feel confident about data processing task with Pandas at advanced level.

What is this course?

This course covers

Basics of Pandas library
Functions of pandas library
making your own data frame using Numpy and pandas
applying data manipulation functions
finding the null values
filling null values using various functions
applying statistical functions

Who this course is for:

python developers curious about data science
everyone who wants to improve their Python programming skills
everyone who wants to improve their data science skills

Pandas library for data science (All in One)

What you'll learn

Explore related topics

Course content

Introduction to pandas3 lectures • 26min

Data manipulation functions in pandas4 lectures • 40min

Making your own data frame and use functions3 lectures • 42min

Statistical function in pandas6 lectures • 1hr 10min

Some more functions in pandas5 lectures • 1hr

Requirements

Description

Who this course is for: