DCIC Mapping Inequality : Baltimore¶

Introduction¶

Importing the Data and Creating a Data Frame¶

In [32]:

# Loads the Pandas library 
import pandas as pd

# Creates data frame (df) by reading in the Baltimore csv
df = pd.read_csv("AD_Data_BaltimoreProject.csv")
df.head()

Out[32]:

	Form	State	City	Security_Grade	Area_Number	Terrain_Description	Favorable_Influences	Detrimental_Influences	INHABITANTS_Type	INHABITANTS_Annual_Income	...	INHABITANTS_Population_Increase	INHABITANTS_Population_Decrease	INHABITANTS_Population_Static	BUILDINGS_Types	BUILDINGS_Construction	BUILDINGS_Age	BUILDINGS_Repair	Ten_Fifteen_Desirability	Remarks	Date
0	NS FORM-8 6-1-37	Maryland	Baltimore	A	2	Rolling	Fairly new suburban area of homogeneous charac...	None	Substantial Middle Class	$3000 - 5,000	...	Fast	NaN	NaN	Detached an row houses	Brick and frame	1 to 10 years	Good	Upward	A recent development with much room for expans...	May 4,1937
1	NS FORM-8 6-1-37	Maryland	Baltimore	A	1	Undulating	Very nicely planned residential area of medium...	None	Executives, Professional Men	over $5000	...	Moderately Fast	NaN	NaN	Single family detached	Brick and Stone	12 years	Very good	Upward	Mostly fee properties. A few homes valued at $...	May 4,1937
2	NS FORM-8 6-1-37	Maryland	Baltimore	A	3	Rolling	Good residential area. Well planned.	Distance to City	Executives, Professional Men	3500 - 7000	...	Moderately Fast	NaN	NaN	One family detached	Brick, Stone, and Frame	1 to 20 years	Good to excellent	Upward	Principally fee property. This section lies in...	May 4,1937
3	NS FORM-8 6-1-37	Maryland	Baltimore	A	4	Level	Well planned development of fairly	None	Professional and Executives	over $5000	...	Slowly	NaN	NaN	One family	Brick, Stone, and Frame	10 years	Good	Upward	All fee property	May 4,1937
4	NS FORM-8 6-1-37	Maryland	Baltimore	A	5	Undulating	Desirable residential section. Good quality, m...	None	Executives, Professional Men	$3,500 - $10,000	...	Moderately Fast	NaN	NaN	One family detached	Brick and Stone	1 to 20 years	Good	Upward	Merridale only recently developed. Prices do n...	NaN

5 rows × 26 columns

In [23]:

# Lists all the columns of the data frame
df.dtypes

Out[23]:

Form                                object
State                               object
City                                object
Security_Grade                      object
Area_Number                          int64
Terrain_Description                 object
Favorable_Influences                object
Detrimental_Influences              object
INHABITANTS_Type                    object
INHABITANTS_Annual_Income           object
INHABITANTS_Foreignborn             object
INHABITANTS_F                      float64
INHABITANTS_Negro                   object
INHABITANTS_N                       object
INHABITANTS_Infiltration            object
INHABITANTS_Relief_Families         object
INHABITANTS_Population_Increase     object
INHABITANTS_Population_Decrease     object
INHABITANTS_Population_Static       object
BUILDINGS_Types                     object
BUILDINGS_Construction              object
BUILDINGS_Age                       object
BUILDINGS_Repair                    object
Ten_Fifteen_Desirability            object
Remarks                             object
Date                                object
dtype: object

Data Cleaning¶

Foreignborn Inhabitants¶

In [33]:

df.INHABITANTS_Foreignborn

Out[33]:

0           No
1         None
2         None
3         None
4         None
5          NaN
6           No
7           No
8           No
9        Small
10    Very few
11          No
12          No
13          No
14          No
15     Mixture
16        None
17         Few
18          No
19          No
20        None
21        None
22          No
23          No
24          No
25         NaN
26         NaN
27         NaN
28          No
29       Small
30        None
31          No
32    Mixture 
33     Mixture
34     Mixture
35          No
36     Mixture
37     Mixture
38     Mixture
39     Mixture
40     Mixture
41     Nominal
42         NaN
43         NaN
44     Nominal
45         NaN
46         NaN
47         NaN
48         NaN
49         NaN
50    Italians
51      Polish
52     Mixture
53     Mixture
54     Mixture
55     Mixture
56     Mixture
Name: INHABITANTS_Foreignborn, dtype: object

In [31]:

# Replaces the values of 'No' with 'None'
df['INHABITANTS_Foreignborn'] = df['INHABITANTS_Foreignborn'].replace('No', 'None')

# Replaces all other values with 'Yes'
for value in df['INHABITANTS_Foreignborn']:
    if value != 'None':
        df['INHABITANTS_Foreignborn'] = df['INHABITANTS_Foreignborn'].replace(value, 'Yes')
df.INHABITANTS_Foreignborn

Out[31]:

0     None
1     None
2     None
3     None
4     None
5      Yes
6     None
7     None
8     None
9      Yes
10     Yes
11    None
12    None
13    None
14    None
15     Yes
16    None
17     Yes
18    None
19    None
20    None
21    None
22    None
23    None
24    None
25     Yes
26     Yes
27     Yes
28    None
29     Yes
30    None
31    None
32     Yes
33     Yes
34     Yes
35    None
36     Yes
37     Yes
38     Yes
39     Yes
40     Yes
41     Yes
42     Yes
43     Yes
44     Yes
45     Yes
46     Yes
47     Yes
48     Yes
49     Yes
50     Yes
51     Yes
52     Yes
53     Yes
54     Yes
55     Yes
56     Yes
Name: INHABITANTS_Foreignborn, dtype: object

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