Completed
- Interpreting our results
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Classroom Contents
Solving Real World Data Science Tasks With Python Pandas
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- 1 - Intro
- 2 - Downloading the Data
- 3 - Getting started with the code Jupyter Notebook
- 4 Task #1: Merging 12 csvs into a single dataframe
- 5 - Read single CSV file
- 6 - List all files in a directory
- 7 - Concatenating files
- 8 - Reading in Updated dataframe
- 9 Task #2: Add a Month column
- 10 - Parse string in Pandas cell .str
- 11 - Drop NaN values from df
- 12 - Remove rows based on condition
- 13 Task #3: Add a sales column
- 14 - Another way to convert a column to numeric ints & floats
- 15 Question #1: What was the best month for sales?
- 16 - Visualizing our results with bar chart in matplotlib
- 17 Question #2: What city sold the most product?
- 18 - Add a city column
- 19 - Using the .apply method super useful!!
- 20 - Why do we use the lambda x ?
- 21 - Dropping a column
- 22 - Answering the question using groupby
- 23 - Plotting our results
- 24 Question #3: What time should we display advertisements to maximize the likelihood of purchases?
- 25 - Using to_datetime method
- 26 - Creating hour & minute columns
- 27 - Matplotlib line graph to plot our results
- 28 - Interpreting our results
- 29 Question #4: What products are most often sold together?
- 30 - Finding duplicate values in our DataFrame
- 31 - Use transform method to join values from two rows into a single row
- 32 - Dropping rows with duplicate values
- 33 - Counting pairs of products itertools, collections
- 34 Question #5: What product sold the most? Why do you think it did?
- 35 - Graphing data
- 36 - Overlaying a second Y-axis on existing chart
- 37 - Interpreting our results
