English | MP4 | AVC 1920×1080 | AAC 48KHz 2ch | 1.5 Hours | 207 MB
Learn how to utilize the pandas module for Python, and become a data ninja, an expert in data analysis!
Learn Pandas (Python 3) And Become A Data Ninja introduces you to the Pandas module/library. It has very quickly grown in functionality, to the point that more companies are relying on it for their data analysis needs. As a result, many of these companies need a data ninja! An expert in data and data analysis!
Let me tell you what you will learn. In the Intro to Pandas section, you’ll learn about the two objects that are the basis of pandas. (Series and DataFrame). In the prepare your data section, I show you how to read .csv and xlsx files, combine dataframes, how to deal with missing data, how to merge, how to order your columns, and much more. In the Time Series section, I go over some time related classes (TimeStamp/DatetimeIndex), how to covert to timestamp, and how to read (pd.read_csv) when dealing with time related data. In the visualization section, you’ll learn how to make a scatter plot and pie plot. Finally, I will also introduce you to Numpy.
What Will I Learn?
- Learn about and utilize the two main objects: Series and DataFrame
- Understand how to prepare and work data. This includes importing, merging, dealing with missing data, and more
- Be able to produce visual representations of your data (ie scatter plot)
Table of Contents
Introduction
1 Introduction
2 Installation process
3 Numpy section
4 Files I mention in lessons
Intro to Pandas
5 Series
6 DataFrame
Preparing and Working with your Data
7 Read CSV and XLSX files
8 Combining DataFrames
9 Dealing with Missing Data
10 Merging
11 Replace Method
12 Creating New Columns Mapping Method
13 Ordering Columns
14 Dropping ColumnsRows
15 Little Exercise
16 Solution
17 Summary Statistics
Time Related ClassesObjects
18 Intro to Time Series classes
19 Converting to Timestamps
20 Reading csv pd.read_csv into DataFrame with Time Series Data
Visualization
21 Scatter Plot
22 Pie Plot
Numpy
23 Creating Arrays
24 Basic Operations
25 Slicing and Indexing
Bonus special offers
26 Bonus Lecture
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