The materials that need to be studied

Book: https://media.readthedocs.org/pdf/pandasguide/latest/pandasguide.pdf

Seaborn: https://elitedatascience.com/python-seaborn-tutorial

Tentative Syllabus: Pandas ---> Numpy ---> Seaborn ---> Scikit-Learn

Comments

Post a Comment

Popular posts from this blog

Lab 3

\ Step 1. Import the necessary libraries In [ ]: Step 2. Import the dataset from this address. https://raw.githubusercontent.com/justmarkham/DAT8/master/data/drinks.csv Step 3. Assign it to a variable called drinks. In [ ]: Step 4. Which continent drinks more beer on average? In [ ]: Step 5. For each continent print the statistics for wine consumption. In [ ]: Step 6. Print the mean alcoohol consumption per continent for every column In [ ]: Step 7. Print the median alcoohol consumption per continent for every column In [ ]: Step 8. Print the mean, min and max values for spirit consumption. This time output a DataFrame In [ ]:
Read more

Lab 10

Step 1. Import the necessary libraries In [ ]: Step 2. Import the dataset from this address. https://github.com/guipsamora/pandas_exercises/blob/master/04_Apply/Students_Alcohol_Consumption/student-mat.csv Step 3. Assign it to a variable called df. In [ ]: Step 4. For the purpose of this exercise slice the dataframe from 'school' until the 'guardian' column In [ ]: Step 5. Create a lambda function that captalize strings. In [ ]: Step 6. Capitalize both Mjob and Fjob In [ ]: Step 7. Print the last elements of the data set. In [ ]: Step 8. Did you notice the original dataframe is still lowercase? Why is that? Fix it and captalize Mjob and Fjob. In [ ]: Step 9. Create a function called majority that return a boolean value to a new column called legal_drinker In [ ]: In [ ]: Step 10. Multiply every number of the dataset by 10. I know this makes no sense, don't forget it is just an exercise In [ ]: In [ ]:
Read more

Lab 2 (Filtering and Sorting)

Step 1. Import the necessary libraries Step 2. This is the data given as a dictionary # Create an example dataframe about a fictional army raw_data = {'regiment': ['Nighthawks', 'Nighthawks', 'Nighthawks', 'Nighthawks', 'Dragoons', 'Dragoons', 'Dragoons', 'Dragoons', 'Scouts', 'Scouts', 'Scouts', 'Scouts'],             'company': ['1st', '1st', '2nd', '2nd', '1st', '1st', '2nd', '2nd','1st', '1st', '2nd', '2nd'],             'deaths': [523, 52, 25, 616, 43, 234, 523, 62, 62, 73, 37, 35],             'battles': [5, 42, 2, 2, 4, 7, 8, 3, 4, 7, 8, 9],             'size': [1045, 957, 1099, 1400, 1592, 1006, 987, 849, 973, 1005, 1099, 1523],             'veterans': [1, 5, 62, 26, 73, 37, 949, 48, 48, 435, 63, 345],             'readines...
Read more