Description
Price: 15.00 USD | Size: 10.4 GB | Duration : 17.11 Hours | 113 Video Lessons
BRAND: Expert TRAINING | ENGLISH | INSTANT DOWNLOAD | 
4.9
Python and Pandas for Data Manipulation Online Training
This intermediate Python and Pandas for Data Manipulation training prepares data practitioners to manipulate and analyze data coming in from multiple sources, big and small, with Pandas.
If it’s your first time stumbling across the name of the Python library that’s used in econometrics for multidimensional structured data set conversion, you should know that Pandas is a deceptively cute name for a really, really powerful data analysis and manipulation tool. If you’re using Python to analyze data, Pandas is arguably the only tool for data munging – transforming raw data into a different format so that it’s more useful.
After this Pandas course, you’ll be selecting, filtering, sorting, cleaning and combining your data quickly and easily.
For anyone who leads an IT team, this open source training can be used to onboard new data practitioners, curated into individual or team training plans, or as an open source reference resource.
Python and Pandas for Data Manipulation: What You Need to Know
This Python and Pandas for Data Manipulation training has videos that cover topics such as:
- Extracting meaning from numbers
- Managing large data sources and extracting the right data from them
- Importing, cleaning, and calculating statistics
- Visualizing data and making smarter decisions
Who Should Take Python and Pandas for Data Manipulation Training?
This Python and Pandas for Data Manipulation training is considered associate-level open source training, which means it was designed for data analysts. This Python skills course is valuable for new IT professionals with at least a year of experience with data science and experienced data practitioners looking to validate their data skills.
New or aspiring data practitioners. There’s almost no time in your data science or data analysis career that’s too early to take Pandas training. Of course, it’s important to learn the fundamentals first without skipping ahead to advanced tools. But as this Pandas course shows, data manipulation and importing isn’t just easier – in some cases, it’s only possible – with Pandas.
Experienced data practitioners. If you’ve been working with data for several years already, you’ve probably seen some of the things Pandas can do. Maybe you didn’t even realize it when you saw them, but Pandas and DataFrames make it possible to extract, filter and transform real-world data at an otherworldly level. This course shows you how to use Pandas and advance your data career.
Python and Pandas for Data Manipulation FAQs: Cost, Training, Value
What will you learn in this Python data manipulation and business intelligence training?
Who should take this Python and Pandas data course?
Is this training in manipulating data with Python and Pandas associated with any certifications?
What certification should you consider after taking this course in using Python and Pandas?
Why should you take this Python data manipulation and business intelligence training?
Installation and Setup
1. Introduction
3 mins
2. Anaconda Installation
10 mins
3. Conda Environments
7 mins
4. Challenge
1 min
5. Challenge Question Answers (optional)
14 mins
Jupyter Notebook
1. Introduction
2 mins
2. Brief History
6 mins
3. Data Types Review
6 mins
4. Cell Types
7 mins
5. Shortcuts
4 mins
6. Code Challenge
1 min
Series Introduction
1. Introduction
2 mins
2. Create a Series from a list
8 mins
3. Create a Series from a dictionary
4 mins
4. Read CSV files
12 mins
5. Read Excel files
7 mins
6. Head and tail functions
5 mins
7. Series attributes
4 mins
8. Series methods
4 mins
Series Attributes and Methods
1. Introduction
1 min
2. Parameter and arguments
9 mins
3. Sorting values
8 mins
4. Series attributes
8 mins
5. Series Methods
8 mins
6. Inplace Mutation
7 mins
7. Sorting Series Indices
7 mins
8. Challenge
1 min
Series Basics
1. Introduction
1 min
2. The in keyword
7 mins
3. Extract by position
7 mins
4. Extract by label
16 mins
5. The get() method
9 mins
6. Math methods
7 mins
7. The idxmin() and idxmax() methods
4 mins
8. Unique values
5 mins
9. The apply() method
6 mins
10. Challenge
1 min
DataFrame Introduction
1. DataFrame Introduction
11 mins
2. Series shared attributes
7 mins
3. Shared methods
8 mins
4. Extracting columns
6 mins
5. Extracting two or more columns
5 mins
6. Adding columns
7 mins
7. Broadcasting Operations
7 mins
8. DataFrames value_counts( )
6 mins
9. Challenge
1 min
DataFrame Cleaning
1. Introduction
1 min
2. Handling null values
7 mins
3. Drop null values
14 mins
4. Impute missing values
7 mins
5. Detect null and not null values
6 mins
6. Challenge
1 min
DataFrame Sorting
1. Introduction
1 min
2. Changing data types
16 mins
3. Sorting values
17 mins
4. Sort by indices
5 mins
5. Ranking a Series
14 mins
6. Challenge
1 min
Filtering Data
1. Introduction
2 mins
2. Optimization
14 mins
3. Conditional Filtering
20 mins
4. Filtering with AND and OR
18 mins
5. Inclusion method
8 mins
6. Challenge
1 min
Filtering Duplicates
1. Introduction
1 min
2. Checking for duplicates
10 mins
3. Drop duplicates
9 mins
4. Unique values
6 mins
5. Inclusion with between()
11 mins
6. Challenge
1 min
7. Solution video
7 mins
Extracting Values
1. Introduction
1 min
2. Setting and resetting indices
14 mins
3. Extraction with loc
14 mins
4. Extraction with iloc
13 mins
5. Setting new values
4 mins
6. Set multiple values
7 mins
7. Challenge
1 min
Extraction Methods
1. Introduction
1 min
2. The drop method
9 mins
3. Returning smallest and largest values
10 mins
4. The where method
8 mins
5. The query method
10 mins
6. The copy method
10 mins
7. Challenge
1 min
Text Data Basics
1. Introduction
1 min
2. Manipulating text data
14 mins
3. String methods
14 mins
4. The replace string method
15 mins
5. Filtering string methods
11 mins
6. Challenge
1 min
Splitting and Stripping Text Data
1. Introduction
2 mins
2. Strip strings
21 mins
3. Column and index methods
7 mins
4. Splitting strings
8 mins
5. More splitting
8 mins
6. Challenge
1 min
Grouping Methods
1. Introduction
1 min
2. Grouping
10 mins
3. group_by operations
11 mins
4. get_group method
9 mins
5. The group_by methods
13 mins
6. Challenge
1 min
Combining DataFrames
1. Introduction
3 mins
2. Combining DataFrames
8 mins
3. Concatenation
21 mins
4. Inner joins
14 mins
5. Outer joins
11 mins
6. Challenge
1 min
Time Series Data
1. Introduction
1 min
2. Python Datetime
9 mins
3. Pandas Timestamp
8 mins
4. DatetimeIndex
6 mins
5. The to_datetime method
11 mins
6. Date Ranges Introduction
8 mins
7. Challenge
1 min
Date Ranges
1. Introduction
1 min
2. Date ranges part 1
28 mins
3. Date ranges part 2
15 mins
4. Date ranges part 3
8 mins
5. The dt accessor
12 mins
6. Challenge
1 min
DataReader
1. Introduction & setup
9 mins
2. Reading cryptocurrency data
16 mins
3. Selecting Datetime rows
12 mins
4. Timestamp attributes & methods
13 mins
5. Challenge
1 min
Visualization
1. Introduction
2 mins
2. Matplotlib & PyPlot
9 mins
3. Customizing visualizations
15 mins
4. Creating charts
13 mins
5. Challenge
1 min
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