The document discusses using Pandas and Jupyter Notebook for data analysis in Python. It introduces Pandas as a data analysis library for working with tabular data like spreadsheets. The key points covered include: - Using Anaconda distribution for a complete Python data analysis environment - Opening Jupyter Notebook and creating cells to run Python code - Loading data from files into Pandas DataFrames for analysis - Filtering and exploring DataFrames using methods like df[] and Counter() - Examples of projects like gathering SEMRush data and custom tracking charts

1. Data Analysis with Pandas

2. When you think of Python...

3. Meet Jupyter Notebook

4. And me
job_title != “Developer”
I’m a Consultant at Distilled (since September 2015)
I do build some software in Python
But I mainly use it for data analysis

5. Getting Started

6. Python for scientific computing
Huge community
Fantastic ecosystem of packages other people have written
Can be tedious to actually install everything

7. Just use this!
(https://continuum.io/downloads)

8. What is Anaconda?
Essentially a large (~400 MB) Python installation
But contains everything* you need for data analysis
Unless you have a special reason not to, you should just
install and use this

9. You need the command line (but only for a minute)
On Windows, open Powershell
On mac, Terminal or iTerm2

10. Just one line, though:
1. Just type “jupyter notebook”
2. Wait
3. ...

11. Back to safety

12. Open a new Notebook

13. Your very own data analysis environment

14. So that was fairly easy...

15. but why is it better than Excel?

16. There’s not enough room to list everything, but:
1. Handle larger data sets—no set limit on rows
2. Combine multiple files and data sources together
instantaneously. Pull data straight from APIs or scraping
3. Everything is completely customisable—if you can
imagine a query, it can be done (though not always easily)
4. It’s a safe place to mess things up

17. ...and it’s the perfect playground for
learning Python

18. Side note: don’t know any Python?

19. Can’t cover it all today, so go here:
1. Learn Python the Hard Way (free)
2. Real Python ($60, but good)
3. Writing Idiomatic Python (~$15)

20. Unless you’re building applications:
1. Stick with the small building blocks
2. Learn how to write a function (we’ll do this today)
3. Learn about loops, conditional statements, and handling
data
4. Probably no need to learn about managing projects and

21. Jupyter Notebook
Save notebooks for later
Run and re-run Python code
Really cool features like post-mortem debugging if you make
a mistake

22. Cells
1. Type all the code you want
2. Shift+Enter to run it
3. View the result

23. Now we have our Jupyter Notebook up and running, you
can start playing around with almost any Python code
We’re going to look at Pandas, though—a data analysis
library written in Python
Started its life in finance
Great for fast, flexible computation
The Star of the Show

24. A little setup, first
You’ll do this more or less at the beginning of each session
It’ll become second nature; just import the workhorse
libraries we always use: numpy, pandas, pyplot.

25. The DataFrame
If you’re used to spreadsheets, the DataFrame isn’t too
difficult to understand
It’s the fundamental, flexible building block in Pandas

26. At its simplest, it looks rather like a spreadsheet would
The only obvious difference with Excel is the column
indexes, which are numeric instead of A, B, C...

27. You’ll usually create them from some other source:
The Pandas library provides some nice functions for
importing from common file formats, so you won’t usually be
building “by hand”:
1. pd.read_csv()
2. pd.read_table()
3. pd.read_sql()

28. We have so much data stored in CSVs
Our first function call will just read some data into the
DataFrame, where we can analyse it
Reading a CSV

29. Get help at any time with Shift+Tab

30. 1. pd.read_csv() will read in the data
2. Fields are separated by tabs
3. The encoding is UTF-16 (don’t ask…)

31. Get a quick sense of the data (658k rows, here)

32. See the columns

33. Filtering

34. What’s happening there?
df[‘Link Active?’] is:
1. Checking that whole column for values that are True or
False
2. Returning an array of True/False values
3. This is fast, and lets us filter in an amazing variety of ways

35. Filtering (again)

36. We’re probably ready for this one, now:

37. Example project: Getting data from
SEMRush

38. Writing your own function

39. Call our function, get a DataFrame!

40. Write to disk in case anything goes wrong

41. Reading in multiple files

42. Apply custom filters

43. Drill down into individual words:
Counter() will save you a huge amount of work
Here we wanted to hone in on modifier words

44. More detailed questions
How local are the searches?
Do people search by state code or full name?
Do people search by hotel category?

45. Second example: Custom Rank Tracking
Charts

46. Where to begin?
If you don’t know Python, start with those books I shared
earlier.
If you do, check out Python for Data Analysis
Keep Jupyter Notebook open at all times
Experiment!

47. Questions?