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],
            'readiness': [1, 2, 3, 3, 2, 1, 2, 3, 2, 1, 2, 3],
            'armored': [1, 0, 1, 1, 0, 1, 0, 1, 0, 0, 1, 1],
            'deserters': [4, 24, 31, 2, 3, 4, 24, 31, 2, 3, 2, 3],
            'origin': ['Arizona', 'California', 'Texas', 'Florida', 'Maine', 'Iowa', 'Alaska', 'Washington', 'Oregon', 'Wyoming', 'Louisana', 'Georgia']}

Step 3. Create a dataframe and assign it to a variable called army.
Don't forget to include the columns names
In [ ]:

Step 4. Set the 'origin' colum as the index of the dataframe
In [ ]:

Step 5. Print only the column veterans
In [ ]:

Step 6. Print the columns 'veterans' and 'deaths'
In [ ]:

Step 7. Print the name of all the columns.
In [ ]:

Step 8. Select the 'deaths', 'size' and 'deserters' columns from Maine and Alaska
In [ ]:

Step 9. Select the rows 3 to 7 and the columns 3 to 6
In [ ]:

Step 10. Select every row after the fourth row
In [ ]:

Step 11. Select every row up to the 4th row
In [ ]:

Step 12. Select the 3rd column up to the 7th column
In [ ]:

Step 13. Select rows where df.deaths is greater than 50
In [ ]:

Step 14. Select rows where df.deaths is greater than 500 or less than 50
In [ ]:

Step 15. Select all the regiments not named "Dragoons"
In [ ]:

Step 16. Select the rows called Texas and Arizona
In [ ]:

Step 17. Select the third cell in the row named Arizona
In [ ]:

Step 18. Select the third cell down in the column named deaths
In [ ]:

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