pandas | ONLINE

Course content

Sections: 26

•

Activities: 0

•

Resources: 269

1. Overview Student FAQ
2. Tips How to get the most out of this course
3. Did you know that....mp4
5. Installation of Anaconda
6. Opening a Jupyter Notebook
7. How to use Jupyter Notebooks
8. How to tackle Pandas Version 1.0
Files

1. Intro to Tabular Data Pandas
2. Download Part 1 Course Materials
1.1 tabdata
2.1 Course Materials Part1

1. Create your very first Pandas DataFrame (from csv)
2. Pandas Display Options and the methods head() & tail()
3. First Data Inspection
4. Built-in Functions, Attributes and Methods with Pandas
5. Make it easy TAB Completion and Tooltip
7. Explore your own Dataset Coding Exercise 1 (Intro)
8. Explore your own Dataset Coding Exercise 1 (Solution)
9. Selecting Columns
10. Selecting one Column with the dot notation
11. Zero-based Indexing and Negative Indexing
12. Selecting Rows with iloc (position-based indexing)
13. Slicing Rows and Columns with iloc (position-based indexing)
15. Selecting Rows with loc (label-based indexing)
16. Slicing Rows and Columns with loc (label-based indexing)
18. Indexing and Slicing with reindex()
19. Summary, Best Practices and Outlook
21. Coding Exercise 2 (Intro)
22. Coding Exercise 2 (Solution)
23. Advanced Indexing and Slicing (optional)

2. First Steps with Pandas Series
3. Analyzing Numerical Series with unique(), nunique() and value_counts()
4. Analyzing non-numerical Series with unique(), nunique(), value_counts()
5. Creating Pandas Series (Part 1)
6. Creating Pandas Series (Part 2)
7. Indexing and Slicing Pandas Series
8. Sorting of Series and Introduction to the inplace - parameter
9. nlargest() and nsmallest()
10. idxmin() and idxmax()
11. Manipulating Pandas Series
14. Coding Exercise 3 (Solution)
15. First Steps with Pandas Index Objects
16. Creating Index Objects from Scratch
17. Changing Row Index with set_index() and reset_index()
18. Changing Column Labels
19. Renaming Index & Column Labels with rename()
22. Coding Exercise 4 (Solution)

2. Filtering DataFrames by one Condition
3. Filtering DataFrames by many Conditions (AND)
4. Filtering DataFrames by many Conditions (OR)
5. Advanced Filtering with between(), isin() and ~
6. any() and all()
7. Removing Columns
8. Removing Rows
9. Adding new Columns to a DataFrame
10. Creating Columns based on other Columns
11. Adding Columns with insert()
12. Creating DataFrames from Scratch with pd.DataFrame()
13. Adding new Rows (hands-on approach)
16. Coding Exercise 5 (Solution)

2. Best Practice (How you should do it)
3. Chained Indexing How you should NOT do it (Part 1)
4. Chained Indexing How you should NOT do it (Part 2)
5. View vs. Copy
6. Simple Rules what to do when...
9. Coding Exercise 6 (Solution)
7. Manipulating DataFrames Slices
8. Coding Exercise 6 (Intro)

2. Sorting DataFrames with sort_index() and sort_values() (Version 1.0 Update)
3. Ranking DataFrames with rank()
4. nunique() and nlargest() nsmallest() with DataFrames
5. Summary Statistics and Accumulations
6. The agg() method.
8. Coding Exercise 7 (Solution)
9. User-defined Functions with apply(), map() and applymap()
10. Hierarchical Indexing (Part 1)
11. Hierarchical Indexing (Part 2)
12. String Operations (Part 1)
13. String Operations (Part 2)
15. Coding Exercise 8 (Solution)

2. The plot() method.
3. Customization of Plots
4. Histograms (Part 1)
5. Histograms (Part 2)
6. Barcharts and Piecharts
7. Scatterplots
9. Coding Exercise 9 (Solution)
1. Intro
8. Coding Exercise 9 (Intro)

2. Download Part 2 Course Materials
1. Welcome to PART 2 Full Data Workflow A-Z
2.1 Course Materials Part2
2.1 Course Materials Part2

1. Importing csv-files with pd.read_csv
2. Importing messy csv-files with pd.read_csv
3. Importing Data from Excel with pd.read_excel()
4. Importing messy Data from Excel with pd.read_excel()
5. Importing Data from the Web with pd.read_html()

1. First Inspection & Handling of inconsistent Data
2. String Operations
3. Changing Datatype of Columns with astype()
4. Intro NA values missing values
5. Detection of missing Values
6. Removing missing values
7. Replacing missing values
8. Intro Duplicates
9. Detection of Duplicates
10. Handling Removing Duplicates
11. The ignore_index parameter (NEW in Pandas 1.0)
12. Detection of Outliers
13. Handling Removing Outliers
14. Categorical Data
15. Pandas Version 1.0 New dtypes and pd.NA
17. Coding Exercise 11 (Solution)

2. Adding Rows with append() and pd.concat() (Part 1).
3. Adding Rows with pd.concat() (Part 2)
4. Arithmetic with Pandas Objects Data Alignment
6. Outer Joins with merge()
7. Inner Joins with merge()
8. Outer Joins (without Intersection) with merge()
9. Left Joins (without Intersection) with merge()
10. Right Joins (without Intersection) with merge()
11. Left Joins with merge()
12. Right Joins with merge()
13. Joining on different Column Names Indexes
14. Joining on more than one Column
15. pd.merge() and join()

1. Intro
2. Understanding the GroupBy Object
3. Splitting with many Keys
4. split-apply-combine explained
5. split-apply-combine applied
7. Advanced aggregation with agg()
8. GroupBy Aggregation with Relabeling (NEW - Pandas Version 0.25)
9. Transformation with transform()
10. Replacing NA Values by group-specific Values
11. Generalizing split-apply-combine with apply()
12. Hierarchical Indexing with Groupby
13. stack() and unstack()
16. Coding Exercise 13 (Solution)

2. Transposing Rows and Columns
3. Pivoting DataFrames with pivot()
4. Limits of pivot()
5. pivot_table()
6. pd.crosstab()
7. melting DataFrames with melt()

2. Arithmetic Operations (Part 1)
3. Arithmetic Operations (Part 2)
4. TransformationMapping with map()
5. Conditional Transformation
6. Discretization and Binning with pd.cut() (Part 1)
7. Discretization and Binning with pd.cut() (Part 2)
8. Discretization and Binning with pd.qcut()
9. Floors and Caps
10. Scaling Standardization
11. Creating Dummy Variables
12. String Operations

2. First Steps in Seaborn
3. Categorical Plots
4. Joint Plots Regression Plots
5. Matrixplots Heatmaps

2. Olympic Medal Tables (Instruction & Hints)
3. Olympic Medal Tables (Solution Part 1)
4. Olympic Medal Tables (Solution Part 2)
5. Olympic Medal Tables (Solution Part 3)
1.1 Course Materials Part3

2.1 Course Materials Part4

1. Importing Time Series Data from csv-files
2. Converting strings to datetime objects with pd.to_datetime()
3. Initial Analysis Visualization of Time Series
4. Indexing and Slicing Time Series
5. Creating a customized DatetimeIndex with pd.date_range()
6. More on pd.date_range()
7. Downsampling Time Series with resample() (Part 1)
8. Downsampling Time Series with resample (Part 2)
9. The PeriodIndex object
10. Advanced Indexing with reindex()

2. Getting Ready (Installing required package)
3. Importing Stock Price Data from Yahoo Finance (it still works!)
4. Initial Inspection and Visualization
5. Normalizing Time Series to a Base Value (100)
6. The shift() method
7. The methods diff() and pct_change()
8. Measuring Stock Performance with MEAN Returns and STD of Returns
9. Financial Time Series - Return and Risk
10. Financial Time Series - Covariance and Correlation
11. Helpful DatetimeIndex Attributes and Methods
12. Filling NA Values with bfill, ffill and interpolation

1. Intro and Overview
4. Important Recap Pandas Display Options (Changed in Version 0.25)
5. Info() method - new and extended output
6. NEW Extension dtypes (nullable dtypes) Why do we need them
8. NEW pd.NA value for missing values
7. Creating the NEW extension dtypes with convert_dtypes()
9. The NEW nullable Int64Dtype
10. The NEW StringDtype
11. The NEW nullable BooleanDtype
12. Addition of the ignore_index parameter
13. Removal of prior Version Deprecations

1. Intro
2. First Steps
3. Variables
4. Data Types Integers and Floats
5. Data Types Strings
6. Data Types Lists (Part 1)
7. Data Types Lists (Part 2)
9. Data Types Sets
10. Operators & Booleans
11. Conditional Statements (if, elif, else, while)
12. For Loops
13. Key words break, pass, continue
14. Generating Random Numbers
15. User Defined Functions (Part 1)
16. User Defined Functions (Part 2)
17. User Defined Functions (Part 3)
18. Visualization with Matplotlib
23. Python Basics/8. Data Types Tuples

1. Introduction to Numpy Arrays
10. Summary Statistics
11. Visualization and (Linear) Regression
13. Numpy Quiz Solution
2. Numpy Arrays Vectorization
3. Numpy Arrays Indexing and Slicing
4. Numpy Arrays Shape and Dimensions
5. Numpy Arrays Indexing and Slicing of multi-dimensional Arrays
6. Numpy Arrays Boolean Indexing
7. Generating Random Numbers
8. Performance Issues
9. Case Study Numpy vs. Python Standard Library

1. Statistics - Overview, Terms and Vocabulary
3. Population vs. Sample
4. Visualizing Frequency Distributions with plt.hist()
5. Relative and Cumulative Frequencies with plt.hist()
6. Measures of Central Tendency (Theory)
7. Coding Measures of Central Tendency - Mean and Median
8. Coding Measures of Central Tendency - Geometric Mean
9. Variability around the Central Tendency Dispersion (Theory)
10. Minimum, Maximum and Range with PythonNumpy
11. Percentiles with PythonNumpy
12. Variance and Standard Deviation with PythonNumpy
13. Skew and Kurtosis (Theory)
14. How to calculate Skew and Kurtosis with scipy.stats
15. How to generate Random Numbers with Numpy
16. Reproducibility with np.random.seed()
17. Probability Distributions - Overview
18. Discrete Uniform Distributions
19. Continuous Uniform Distributions
20. The Normal Distribution (Theory)
21. Creating a normally distributed Random Variable
22. Normal Distribution - Probability Density Function (pdf) with scipy.stats
23. Normal Distribution - Cumulative Distribution Function (cdf) with scipy.stats
24. The Standard Normal Distribution and Z-Values
25. Properties of the Standard Normal Distribution (Theory)
26. Probabilities and Z-Values with scipy.stats
27. Confidence Intervals with scipy.stats
37. Case Study (Part 2) The Market Model (Single Factor Model)
28. Covariance and Correlation Coefficient (Theory)
29. Cleaning and preparing the Data - Movies Database (Part 1)
30. Cleaning and preparing the Data - Movies Database (Part 2)
31. How to calculate Covariance and Correlation in Python
32. Correlation and Scatterplots – visual Interpretation
33. What is Linear Regression (Theory)
34. A simple Linear Regression Model with numpy & Scipy
35. How to interpret Intercept and Slope Coefficient
36. Case Study (Part 1) The Market Model (Single Factor Model)
Files

Files

1. Get your special BONUS here!

Enrolment options

Udemy-The Complete Pandas Bootcamp 2020 Data Science with Python

Course modified date: 9 Feb 2024

Enrolled students: There are no students enrolled in this course.

Guests cannot access this course. Please log in.

Udemy-The Complete Pandas Bootcamp 2020 Data Science with Python

About the course

Course content

Instructors

Enrolment options

Udemy-The Complete Pandas Bootcamp 2020 Data Science with Python

This course includes

Share this course