Chapter 30 Short pandas Exercises

30.0.0.1 DataFrame Creation:

Create a Pandas DataFrame with two columns, “Name” and “Age”, with three rows of data: names of your choice and corresponding ages.

import pandas as pd

data = {"Name": ["alice", "betty", "adam"],
        "Age": [22, 24, 21]}
        
df = pd.DataFrame(data)
df

##     Name  Age
## 0  alice   22
## 1  betty   24
## 2   adam   21

30.0.0.2 Selecting Data:

Given a DataFrame df with columns “Product”, “Price”, and “Quantity”, how would you select only the “Price” column?

data = {"Product": ["x", "y", "z"],
        "Price": [10, 15, 13],
        "Quantity": [20, 50, 100]}
        
df = pd.DataFrame(data)

## selecting a column and return data frame
df[["Price"]]

##    Price
## 0     10
## 1     15
## 2     13

30.0.0.3 Filtering Rows:

Using the DataFrame df from question 2, write a command to filter for rows where “Price” is greater than $10.

df[df.Price > 10]

##   Product  Price  Quantity
## 1       y     15        50
## 2       z     13       100

df.query("Price > 10")

##   Product  Price  Quantity
## 1       y     15        50
## 2       z     13       100

30.0.0.4 Adding a New Column:

In a DataFrame with columns “Length” and “Width”, how would you create a new column called “Area” that multiplies “Length” and “Width” for each row?

data = {"width": [2, 3, 4], "height": [10, 12, 10]}
df = pd.DataFrame(data)

## area column
df['area'] = df['width']*df['height']

df.head()

##    width  height  area
## 0      2      10    20
## 1      3      12    36
## 2      4      10    40

30.0.0.5 GroupBy Operation:

Suppose you have a DataFrame with columns “Category” and “Sales”. How would you group the data by “Category” and calculate the total “Sales” for each category?

categories = ["luxury", "medium", "premium", "medium", "premium","luxury", "medium"]
sales = [5, 3, 10, 30, 2, 5, 10]
data = {"category": categories, "sales": sales}
df = pd.DataFrame(data)

## group-by
df.groupby('category')['sales'].sum()

## category
## luxury     10
## medium     43
## premium    12
## Name: sales, dtype: int64

## group-by and new column

Handling Missing Data: If a DataFrame df has some missing values, what command would you use to fill these with a default value, such as 0? Sorting Data: In a DataFrame df with columns “Date” and “Sales”, how would you sort the rows by “Sales” in descending order?