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آموزش اصولی pandas 

دسته بندی ها: آموزش Pandas ، آموزش های لیندا (Lynda)
به این نوشته امتیاز دهید 1 2 3 4 5 بدون امتیاز
pandas Essential Training Publisher:Lynda Author:Jonathan Fernandes Duration:2h 14m Level:Intermediate

Discover how to work with the pandas library and tools for data analysis and data structuring.
Released: 11/3/2017

pandas is an open-source library that provides high-performance, easy-to-use data structures, and data analysis tools for Python. In this intermediate-level, hands-on course, learn how to use the pandas library and tools for data analysis and data structuring. Instructor Jonathan Fernandes dives into topics such as DataFrames, basic plotting, indexing, and groupby. To help you learn how to work with data more effectively, Jonathan takes you through a series of exercises that are based on the same large, public data set: the Olympic medal winners from 1896 to 2008.

Topics include:
DataFrames
Working with plots
Boolean indexing
String handling
Indexing
Grouping data
Reshaping
Creating your own colormaps

Introduction
Welcome
1m
What you should know
31s
Exercise files
32s
1. Technical Setup
Installing Anaconda
2m 59s
Downloading the data set
1m 37s
Using the Jupyter notebook
3m 59s
Using Pandas
2m 51s
2. Series and DataFrames
DataFrames
1m 19s
Series
4m 32s
Challenge
41s
Solution
2m 41s
3. Data Input and Validation
Using read_csv()
3m 24s
Using shape
1m 25s
Using head() and tail()
1m 48s
Using info()
1m 2s
4. Basic Analysis
Using value_counts()
3m 13s
Using sort_values()
3m 33s
Boolean indexing
2m 30s
String handling
1m 33s
Challenge
1m 15s
Solution
7m 40s
5. Basic Plotting
Basic plotting
1m 39s
Plot types
4m 10s
Colors
1m 1s
Figsize
40s
Colormaps
1m 29s
Seaborn basic plotting
3m 29s
Challenge
52s
Solution
5m 15s
6. Indexing
Index
1m 30s
Using set_index()
2m 51s
Using reset_index()
1m 23s
Using sort_index()
2m 25s
Using loc[]
2m 7s
Using iloc[]
2m 3s
Challenge
33s
Solution
7m 39s
7. Groupby
Groupby
2m 9s
Iterate through a group
1m 56s
Groupby computations
5m 55s
Challenge
38s
Solution
3m 43s
8. Reshaping
Reshaping
3m 34s
Using stack()
1m 41s
Using unstack()
1m 27s
Challenge
43s
Solution
6m 7s
9. Data Visualizations
Learning heatmaps
5m 38s
Creating your own colormaps
4m 31s
10. Challenge
Final challenge
34s
Final solution
6m
Conclusion
Next steps
1m 1s

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