pandas

Question 1

import pandas library

Answer 1

import pandas as pd

Question 2

create a hardcoded dataframe

Answer 2

dfHardCoded = pd.DataFrame(
    # records
    [
        ['Jan', 23, 62],
        ['Feb', 11, 50],
        ['Mar', 40, 45],
        ['Apr', 22, 26],
        ['May', 40, 60],
        ['Jun', 10, 62]
    ],
    # indices
    index = [ 0, 1, 2, 3, 4, 5],
    # column headers
    columns = [ "month", "lowest", "highest"]
  )

Question 3

read data.csv. It is a TSV

Answer 3

df = pd.read_csv(“data.csv”, sep=”\t”)

Question 4

descriptive statistics table for dataframe “df”

Answer 4

df.describe()

Question 5

first 5 elements of “df”

Answer 5

df.head()

Question 6

last 10 elements of “df”

Answer 6

df.tail(10)

Question 7

datatypes of “df”

Answer 7

df.dtypes

Question 8

information about the indices of records of “df”

Answer 8

df.index

Question 9

values of “df” (i.e. records) as a numpy array

Answer 9

df.values

Question 10

sort values of “df” based on feature “color”, descending

Answer 10

df.sort(“color’”, ascending=False)

Question 11

return “color” column of dataframe as a pandas series object

Answer 11

df[“color”]

Question 12

return “color” and “price” columns of dataframe as a pandas dataframe object

Answer 12

df[“color”, “price”]

Question 13

return a range of records between indices 13-50

Answer 13

df[13:51]

Question 14

return “color” at indices 2 and 3

Answer 14

df.loc[2:4,[“color”]]

Question 15

return price at indices 3 and 4. You don’t know the exact name of the price column, but you know it’s index is 7 in the array of columns

Answer 15

df.iloc[3:5,[7]]

Question 16

return all records with “age” below 10

Answer 16

df[df.age < 10]

Question 17

return all records with “month” being “Jan” or “Feb”

Answer 17

df[df.month.isin([“Jan”, “Feb”])]

Question 18

assign 0 to “score” at records 50 to 100

Answer 18

df.loc[50:101, [“score”]] = 0

Question 19

export to “hello.csv”. Make it tab delimited

Answer 19

df.to_csv(“hello.csv”, sep=”\t”)