SQL Python Practice Pricing

Python fundamentals pandas NumPy

pandas course

Clean, combine and analyse tables of data with pandas, step by step, on simulated company data.

0.1Your first DataFrame
0.2Columns, not loops

1.1Series
1.2Building DataFrames
1.3Reading CSV files
1.4Inspecting data

2.1Selecting columns
2.2loc and iloc
2.3Filtering rows
2.4isin, between and query
2.5Sorting and top N

3.1New columns
3.2Conditional columns
3.3Renaming, dropping and chaining
3.4Fixing types

4.1Missing values
4.2Duplicates

5.1groupby
5.2Named aggregations
5.3transform
5.4Top N per group and group filters
5.5value_counts and crosstab

6.1merge
6.2merge pitfalls
6.3concat

7.1pivot_table
7.2melt
7.3stack and unstack

8.1Dates and the .dt accessor
8.2resample and missing days
8.3Time between events

9.1Text columns with .str
9.2split, extract and explode

10.1shift, diff and pct_change
10.2Running totals
10.3Rolling averages
10.4rank

11.1map and apply
11.2Vectorise instead of apply

12.1Funnels
12.2Cohort retention
12.3Reading an A/B test
12.4Latest record per key
12.5Mini project

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Company names only describe the kind of business in each exercise. All practice data is simulated, and the interviews and problems are our own, not real interview questions. We aren't affiliated with or endorsed by any company named.