# Answers -In Class Activity - More Pandas and data processing¶

First, let’s import our packages.

import pandas as pd
import numpy as np
import numpy.random as npr
import math


Second, we want to read in a csv file in the dataframe df. The file has a list of various faculty members and their phone numbers (don’t worry, the phone numbers are randomly generated. So best not try calling them). We also want to tell df that the phone numbers are actually strings rather than traditional numbers, which we do with the astype method.

df = pd.read_csv('faculty.csv')
df['Phone'] = df['Phone'].astype(str)
df

Last name First name Phone
1 Amodio David 3803269359
2 Balcetis Emily 2204342277
3 Van Bavel Jay 3252450008
4 Carrasco Marisa 2092652228
5 Cimpian Andrei 6966417505
6 Craig Maureen 7022020569
7 Curtis Clay 7768322051
8 Dillon Moira 6209318809
9 Freeman Jon 4746939756
10 Gollwitzer Peter 3195372989

## Problem 0: Changing the format of a phone number¶

For this problem, we want to convert the phone numbers to a more readable format [ 9285162643 $$\rightarrow$$ (928)516-2643 ]. You may have experience doing this sort of thing by hand with Excel, which can be very cumbersome and error-prone. Let’s see how to do this with pandas instead.

Please write code to make the transformation to (ABC)DEF-HIJK format for each phone number in df.

Hint: Write a function convert_phone that converts the format of a single phone number (also, remind yourself about Python list slicing). Then, you can apply that function using the transform operation described in book section 6.12.1.2.

# Your answer goes here

def convert_phone(x):
assert(len(x)==10)
return '(' + x[:3] + ')' + x[3:6] + '-' + x[6:]

df['Phone'] = df['Phone'].transform(convert_phone)


## Problem 1: Making a new column¶

Using the same dataframe df, make a new column that lists the complete name of each professor. For instance, the new column should be called ‘Complete name’ and the first entry should be the string ‘Karen Adolph’.

Hint: You could make a new function and use the same logic as above. Alternatively, you can also try directly summing the relevant columns.

# Your answer here
df['Complete name'] = df['First name'] + ' ' + df['Last name']
df

Last name First name Phone Complete name
1 Amodio David 3803269359 David Amodio
2 Balcetis Emily 2204342277 Emily Balcetis
3 Van Bavel Jay 3252450008 Jay Van Bavel
4 Carrasco Marisa 2092652228 Marisa Carrasco
5 Cimpian Andrei 6966417505 Andrei Cimpian
6 Craig Maureen 7022020569 Maureen Craig
7 Curtis Clay 7768322051 Clay Curtis
8 Dillon Moira 6209318809 Moira Dillon
9 Freeman Jon 4746939756 Jon Freeman
10 Gollwitzer Peter 3195372989 Peter Gollwitzer

## Problem 2: Computing the area and creating a new column¶

Let’s create the rectangle and circle dataframe from last week and call it df_shapes.

mytype = np.array(['rectangle','circle','rectangle','rectangle','circle','rectangle','circle','rectangle','circle','circle'])
width = npr.rand(len(mytype))*10.
height = npr.rand(len(mytype))*10.
height[mytype=='circle']=np.nan
df_shapes = pd.DataFrame({"type":mytype, "width":width, "height":height})
df_shapes

type width height
0 rectangle 0.144597 5.902562
1 circle 0.944694 NaN
2 rectangle 9.350486 3.828103
3 rectangle 7.251338 0.301459
4 circle 1.120220 NaN
5 rectangle 8.768920 8.915431
6 circle 0.582969 NaN
7 rectangle 2.239293 0.090153
8 circle 7.015915 NaN
9 circle 6.086628 NaN

Next, you should compute the mean ‘width’ separately for the rectangles and circles.

Hint: you can use groupby and .mean() from chapter 6.12.

# Your answer here
df_shapes.groupby('type')['width'].mean()