import pandas as pd
import numpy as npData Cleaning with Python
Extra Writeups
Links
- This is the Jupyter notebook I created during the Video Walkthrough
- Run on Colab to pause the video and edit interactively!
In [1]:
(1) Overview
A series of longitudinal studies in the Journal of the American Medical Association (JAMA), most recently Reuben et al. (2019) direct link, have found consistent and robust associations between childhood exposure to lead and adult psychopathology:
In this multidecade, longitudinal study of lead-exposed children, higher childhood blood lead level was associated with greater psychopathology across the life course and difficult adult personality traits. Childhood lead exposure may have long-term consequences for adult mental health and personality. (Reuben et al. 2019)
Though we won’t explore that association directly here, I wanted to gather two datasets that would be relevant for studying it, to demonstrate some basic data-cleaning steps using Python libraries like pandas and openpyxl.
If you’re interested in applying your DSAN 5000 skills to the healthcare sphere, definitely consider studying this issue! I have a close friend, for example, who teaches at a school in East Oakland which is currently facing a lead contamination crisis. As a third dataset on top of the two we clean here, for example, you could try finding data on income by neighborhood—there is a reason why lead contamination is continually “discovered” at schools in neighborhoods like East Oakland and West Baltimore, and not at schools in neighborhoods like Georgetown.
First, since the .csv filenames are super long, let’s just use one of my favorite Python libraries, glob, to automatically get a list of all the .csv files within the same folder as this notebook, so we don’t even have to manually type the name(s) of the data files in our code!
In [2]:
import glob
glob.glob("data/*")['data/SHIP_Emergency_Department_Visits_Related_To_Mental_Health_Conditions_2008-2017.csv',
'data/fakedata.dat',
'data/MD_CountyLevelSummary_2017.xlsx']In [3]:
csv_fpaths = glob.glob("data/*.csv")
xlsx_fpaths = glob.glob("data/*.xlsx")
data_fpaths = csv_fpaths + xlsx_fpaths
# for fpath in all_data_fpaths:
# print(fpath)
for fpath_index, fpath in enumerate(data_fpaths):
print(fpath_index, fpath)0 data/SHIP_Emergency_Department_Visits_Related_To_Mental_Health_Conditions_2008-2017.csv
1 data/MD_CountyLevelSummary_2017.xlsxIn [4]:
data_fpaths[0]'data/SHIP_Emergency_Department_Visits_Related_To_Mental_Health_Conditions_2008-2017.csv'(3) All Aboard the SHIP
SHIP stands (weirdly) for State Health Insurance Assistance Program, a program in the state of Maryland which subsidizes health insurance payments for low-income healthcare recipients. You can find the full info and codebook for the SHIP data here
Here we also see a third way to use glob(): somewhat like tab completion in the Linux shell, we can type the beginning of the filename followed by the wildcard character * to obtain the full filename we need. Note that glob.glob() always returns a list, so we need to add [0] at the end to access the first result (even if there is only one result in total!).
In [5]:
ship_fpath = glob.glob("data/SHIP*.csv")[0]
ship_df = pd.read_csv(ship_fpath)
ship_df.head()| Jurisdiction | Value | Race/ ethnicity | Year | Measure | |
|---|---|---|---|---|---|
| 0 | State | 4291.5 | All races/ ethnicities (aggregated) | 2017 | Mental Health ED visits |
| 1 | Allegany | 3309.6 | All races/ ethnicities (aggregated) | 2017 | Mental Health ED visits |
| 2 | Anne Arundel | 5734.1 | All races/ ethnicities (aggregated) | 2017 | Mental Health ED visits |
| 3 | Baltimore City | 10093.5 | All races/ ethnicities (aggregated) | 2017 | Mental Health ED visits |
| 4 | Baltimore County | 4210.1 | All races/ ethnicities (aggregated) | 2017 | Mental Health ED visits |
(4) Lead Levels Data from the CDC
Especially given
In [6]:
%pip install openpyxlRequirement already satisfied: openpyxl in /Users/jpj/.pyenv/versions/3.11.5/lib/python3.11/site-packages (3.1.2)
Requirement already satisfied: et-xmlfile in /Users/jpj/.pyenv/versions/3.11.5/lib/python3.11/site-packages (from openpyxl) (1.1.0)
Note: you may need to restart the kernel to use updated packages.And data on childhood lead levels can be found from the CDC website’s data portal. The most recent sample was conducted in 2021, the results of which are contained in the Excel-format dataset we load here.
In [7]:
xlsx_fpath = glob.glob("data/*.xlsx")[0]
places_df = pd.read_excel(xlsx_fpath, skiprows=3)
places_df.rename(columns={'Unnamed: 0': 'fips', 'Unnamed: 1': 'county'}, inplace=True)
places_df = places_df.rename(columns={'Unnamed: 0': 'fips', 'Unnamed: 1': 'county'})
rename_map = {
'Unnamed: 0': 'fips',
'Unnamed: 1': 'county',
#'Unnamed: 2': 'child_pop',
'Percent': 'pct_lead'
}
places_df.rename(columns=rename_map, inplace=True)In [8]:
cols_to_keep = list(rename_map.values())
places_df = places_df[cols_to_keep]In [9]:
places_df| fips | county | pct_lead | |
|---|---|---|---|
| 0 | 001 | Allegany County | 0.020888 |
| 1 | 003 | Anne Arundel County | 0.003220 |
| 2 | 005 | Baltimore County | 0.009980 |
| 3 | 009 | Calvert County | 0.006550 |
| 4 | 011 | Caroline County | 0.019973 |
| 5 | 013 | Carroll County | 0.006377 |
| 6 | 015 | Cecil County | 0.006893 |
| 7 | 017 | Charles County | 0.002288 |
| 8 | 019 | Dorchester County | 0.027439 |
| 9 | 021 | Frederick County | 0.003054 |
| 10 | 023 | Garrett County | NaN |
| 11 | 025 | Harford County | 0.003109 |
| 12 | 027 | Howard County | 0.008291 |
| 13 | 029 | Kent County | NaN |
| 14 | 031 | Montgomery County | 0.003826 |
| 15 | 033 | Prince George's County | 0.012376 |
| 16 | 035 | Queen Anne's County | NaN |
| 17 | 037 | St. Mary's County | NaN |
| 18 | 039 | Somerset County | 0.015837 |
| 19 | 041 | Talbot County | 0.012442 |
| 20 | 043 | Washington County | 0.008881 |
| 21 | 045 | Wicomico County | 0.013187 |
| 22 | 047 | Worcester County | 0.012959 |
| 23 | 510 | Baltimore (city) County | 0.041661 |
| 24 | NaN | Unknown | NaN |
| 25 | NaN | NaN | NaN |
| 26 | Notes: 'N/A' indicates data are supressed when... | NaN | NaN |
| 27 | Population estimates calculated as population ... | NaN | NaN |
| 28 | Data received and processed by CDC as of April... | NaN | NaN |
First, we note that only the rows from index 0 to index 23 contain the data we want (the rest contain footnotes added to the cells below the main dataset). So, we use the .iloc accessor from Pandas to “slice” just these first 24 rows, keeping in mind that Python’s slice operator : is inclusive-exclusive, meaning that:
- The number before the
:will be the first index included, but - The number after the
:will be one greater than the last index included
In [10]:
places_df = places_df.iloc[0:24].copy()In [11]:
places_df| fips | county | pct_lead | |
|---|---|---|---|
| 0 | 001 | Allegany County | 0.020888 |
| 1 | 003 | Anne Arundel County | 0.003220 |
| 2 | 005 | Baltimore County | 0.009980 |
| 3 | 009 | Calvert County | 0.006550 |
| 4 | 011 | Caroline County | 0.019973 |
| 5 | 013 | Carroll County | 0.006377 |
| 6 | 015 | Cecil County | 0.006893 |
| 7 | 017 | Charles County | 0.002288 |
| 8 | 019 | Dorchester County | 0.027439 |
| 9 | 021 | Frederick County | 0.003054 |
| 10 | 023 | Garrett County | NaN |
| 11 | 025 | Harford County | 0.003109 |
| 12 | 027 | Howard County | 0.008291 |
| 13 | 029 | Kent County | NaN |
| 14 | 031 | Montgomery County | 0.003826 |
| 15 | 033 | Prince George's County | 0.012376 |
| 16 | 035 | Queen Anne's County | NaN |
| 17 | 037 | St. Mary's County | NaN |
| 18 | 039 | Somerset County | 0.015837 |
| 19 | 041 | Talbot County | 0.012442 |
| 20 | 043 | Washington County | 0.008881 |
| 21 | 045 | Wicomico County | 0.013187 |
| 22 | 047 | Worcester County | 0.012959 |
| 23 | 510 | Baltimore (city) County | 0.041661 |
In [12]:
places_df['county'] = places_df['county'].str.replace(" County","")In [13]:
places_df| fips | county | pct_lead | |
|---|---|---|---|
| 0 | 001 | Allegany | 0.020888 |
| 1 | 003 | Anne Arundel | 0.003220 |
| 2 | 005 | Baltimore | 0.009980 |
| 3 | 009 | Calvert | 0.006550 |
| 4 | 011 | Caroline | 0.019973 |
| 5 | 013 | Carroll | 0.006377 |
| 6 | 015 | Cecil | 0.006893 |
| 7 | 017 | Charles | 0.002288 |
| 8 | 019 | Dorchester | 0.027439 |
| 9 | 021 | Frederick | 0.003054 |
| 10 | 023 | Garrett | NaN |
| 11 | 025 | Harford | 0.003109 |
| 12 | 027 | Howard | 0.008291 |
| 13 | 029 | Kent | NaN |
| 14 | 031 | Montgomery | 0.003826 |
| 15 | 033 | Prince George's | 0.012376 |
| 16 | 035 | Queen Anne's | NaN |
| 17 | 037 | St. Mary's | NaN |
| 18 | 039 | Somerset | 0.015837 |
| 19 | 041 | Talbot | 0.012442 |
| 20 | 043 | Washington | 0.008881 |
| 21 | 045 | Wicomico | 0.013187 |
| 22 | 047 | Worcester | 0.012959 |
| 23 | 510 | Baltimore (city) | 0.041661 |
In [14]:
places_df['county'] = places_df['county'].str.replace('(city)', 'City', regex=False)In [15]:
places_df| fips | county | pct_lead | |
|---|---|---|---|
| 0 | 001 | Allegany | 0.020888 |
| 1 | 003 | Anne Arundel | 0.003220 |
| 2 | 005 | Baltimore | 0.009980 |
| 3 | 009 | Calvert | 0.006550 |
| 4 | 011 | Caroline | 0.019973 |
| 5 | 013 | Carroll | 0.006377 |
| 6 | 015 | Cecil | 0.006893 |
| 7 | 017 | Charles | 0.002288 |
| 8 | 019 | Dorchester | 0.027439 |
| 9 | 021 | Frederick | 0.003054 |
| 10 | 023 | Garrett | NaN |
| 11 | 025 | Harford | 0.003109 |
| 12 | 027 | Howard | 0.008291 |
| 13 | 029 | Kent | NaN |
| 14 | 031 | Montgomery | 0.003826 |
| 15 | 033 | Prince George's | 0.012376 |
| 16 | 035 | Queen Anne's | NaN |
| 17 | 037 | St. Mary's | NaN |
| 18 | 039 | Somerset | 0.015837 |
| 19 | 041 | Talbot | 0.012442 |
| 20 | 043 | Washington | 0.008881 |
| 21 | 045 | Wicomico | 0.013187 |
| 22 | 047 | Worcester | 0.012959 |
| 23 | 510 | Baltimore City | 0.041661 |
In [16]:
ship_df['Year'] == 20170 True
1 True
2 True
3 True
4 True
...
1245 False
1246 False
1247 False
1248 False
1249 False
Name: Year, Length: 1250, dtype: boolIn [17]:
ship_df = ship_df.loc[ship_df['Year'] == 2017]In [18]:
ship_df = ship_df[ship_df['Jurisdiction'] != "State"]In [19]:
ship_df = ship_df[ship_df['Race/ ethnicity'] == "All races/ ethnicities (aggregated)"]
ship_df| Jurisdiction | Value | Race/ ethnicity | Year | Measure | |
|---|---|---|---|---|---|
| 1 | Allegany | 3309.6 | All races/ ethnicities (aggregated) | 2017 | Mental Health ED visits |
| 2 | Anne Arundel | 5734.1 | All races/ ethnicities (aggregated) | 2017 | Mental Health ED visits |
| 3 | Baltimore City | 10093.5 | All races/ ethnicities (aggregated) | 2017 | Mental Health ED visits |
| 4 | Baltimore County | 4210.1 | All races/ ethnicities (aggregated) | 2017 | Mental Health ED visits |
| 5 | Calvert | 2999.1 | All races/ ethnicities (aggregated) | 2017 | Mental Health ED visits |
| 6 | Caroline | 7556.2 | All races/ ethnicities (aggregated) | 2017 | Mental Health ED visits |
| 7 | Carroll | 4216.0 | All races/ ethnicities (aggregated) | 2017 | Mental Health ED visits |
| 8 | Cecil | 9584.2 | All races/ ethnicities (aggregated) | 2017 | Mental Health ED visits |
| 9 | Charles | 2817.6 | All races/ ethnicities (aggregated) | 2017 | Mental Health ED visits |
| 10 | Dorchester | 11251.8 | All races/ ethnicities (aggregated) | 2017 | Mental Health ED visits |
| 11 | Frederick | 3064.1 | All races/ ethnicities (aggregated) | 2017 | Mental Health ED visits |
| 12 | Garrett | 7967.6 | All races/ ethnicities (aggregated) | 2017 | Mental Health ED visits |
| 13 | Harford | 3020.2 | All races/ ethnicities (aggregated) | 2017 | Mental Health ED visits |
| 14 | Howard | 3082.1 | All races/ ethnicities (aggregated) | 2017 | Mental Health ED visits |
| 15 | Kent | 13662.1 | All races/ ethnicities (aggregated) | 2017 | Mental Health ED visits |
| 16 | Montgomery | 2312.1 | All races/ ethnicities (aggregated) | 2017 | Mental Health ED visits |
| 17 | Prince George's | 1955.6 | All races/ ethnicities (aggregated) | 2017 | Mental Health ED visits |
| 18 | Queen Anne's | 6119.5 | All races/ ethnicities (aggregated) | 2017 | Mental Health ED visits |
| 19 | Saint Mary's | 6173.1 | All races/ ethnicities (aggregated) | 2017 | Mental Health ED visits |
| 20 | Somerset | 2696.1 | All races/ ethnicities (aggregated) | 2017 | Mental Health ED visits |
| 21 | Talbot | 7661.6 | All races/ ethnicities (aggregated) | 2017 | Mental Health ED visits |
| 22 | Washington | 5410.8 | All races/ ethnicities (aggregated) | 2017 | Mental Health ED visits |
| 23 | Wicomico | 2897.6 | All races/ ethnicities (aggregated) | 2017 | Mental Health ED visits |
| 24 | Worcester | 3502.8 | All races/ ethnicities (aggregated) | 2017 | Mental Health ED visits |
In [20]:
ship_df| Jurisdiction | Value | Race/ ethnicity | Year | Measure | |
|---|---|---|---|---|---|
| 1 | Allegany | 3309.6 | All races/ ethnicities (aggregated) | 2017 | Mental Health ED visits |
| 2 | Anne Arundel | 5734.1 | All races/ ethnicities (aggregated) | 2017 | Mental Health ED visits |
| 3 | Baltimore City | 10093.5 | All races/ ethnicities (aggregated) | 2017 | Mental Health ED visits |
| 4 | Baltimore County | 4210.1 | All races/ ethnicities (aggregated) | 2017 | Mental Health ED visits |
| 5 | Calvert | 2999.1 | All races/ ethnicities (aggregated) | 2017 | Mental Health ED visits |
| 6 | Caroline | 7556.2 | All races/ ethnicities (aggregated) | 2017 | Mental Health ED visits |
| 7 | Carroll | 4216.0 | All races/ ethnicities (aggregated) | 2017 | Mental Health ED visits |
| 8 | Cecil | 9584.2 | All races/ ethnicities (aggregated) | 2017 | Mental Health ED visits |
| 9 | Charles | 2817.6 | All races/ ethnicities (aggregated) | 2017 | Mental Health ED visits |
| 10 | Dorchester | 11251.8 | All races/ ethnicities (aggregated) | 2017 | Mental Health ED visits |
| 11 | Frederick | 3064.1 | All races/ ethnicities (aggregated) | 2017 | Mental Health ED visits |
| 12 | Garrett | 7967.6 | All races/ ethnicities (aggregated) | 2017 | Mental Health ED visits |
| 13 | Harford | 3020.2 | All races/ ethnicities (aggregated) | 2017 | Mental Health ED visits |
| 14 | Howard | 3082.1 | All races/ ethnicities (aggregated) | 2017 | Mental Health ED visits |
| 15 | Kent | 13662.1 | All races/ ethnicities (aggregated) | 2017 | Mental Health ED visits |
| 16 | Montgomery | 2312.1 | All races/ ethnicities (aggregated) | 2017 | Mental Health ED visits |
| 17 | Prince George's | 1955.6 | All races/ ethnicities (aggregated) | 2017 | Mental Health ED visits |
| 18 | Queen Anne's | 6119.5 | All races/ ethnicities (aggregated) | 2017 | Mental Health ED visits |
| 19 | Saint Mary's | 6173.1 | All races/ ethnicities (aggregated) | 2017 | Mental Health ED visits |
| 20 | Somerset | 2696.1 | All races/ ethnicities (aggregated) | 2017 | Mental Health ED visits |
| 21 | Talbot | 7661.6 | All races/ ethnicities (aggregated) | 2017 | Mental Health ED visits |
| 22 | Washington | 5410.8 | All races/ ethnicities (aggregated) | 2017 | Mental Health ED visits |
| 23 | Wicomico | 2897.6 | All races/ ethnicities (aggregated) | 2017 | Mental Health ED visits |
| 24 | Worcester | 3502.8 | All races/ ethnicities (aggregated) | 2017 | Mental Health ED visits |
In [21]:
ship_df['Jurisdiction'] = ship_df['Jurisdiction'].str.replace("Baltimore County", "Baltimore")In [22]:
ship_df| Jurisdiction | Value | Race/ ethnicity | Year | Measure | |
|---|---|---|---|---|---|
| 1 | Allegany | 3309.6 | All races/ ethnicities (aggregated) | 2017 | Mental Health ED visits |
| 2 | Anne Arundel | 5734.1 | All races/ ethnicities (aggregated) | 2017 | Mental Health ED visits |
| 3 | Baltimore City | 10093.5 | All races/ ethnicities (aggregated) | 2017 | Mental Health ED visits |
| 4 | Baltimore | 4210.1 | All races/ ethnicities (aggregated) | 2017 | Mental Health ED visits |
| 5 | Calvert | 2999.1 | All races/ ethnicities (aggregated) | 2017 | Mental Health ED visits |
| 6 | Caroline | 7556.2 | All races/ ethnicities (aggregated) | 2017 | Mental Health ED visits |
| 7 | Carroll | 4216.0 | All races/ ethnicities (aggregated) | 2017 | Mental Health ED visits |
| 8 | Cecil | 9584.2 | All races/ ethnicities (aggregated) | 2017 | Mental Health ED visits |
| 9 | Charles | 2817.6 | All races/ ethnicities (aggregated) | 2017 | Mental Health ED visits |
| 10 | Dorchester | 11251.8 | All races/ ethnicities (aggregated) | 2017 | Mental Health ED visits |
| 11 | Frederick | 3064.1 | All races/ ethnicities (aggregated) | 2017 | Mental Health ED visits |
| 12 | Garrett | 7967.6 | All races/ ethnicities (aggregated) | 2017 | Mental Health ED visits |
| 13 | Harford | 3020.2 | All races/ ethnicities (aggregated) | 2017 | Mental Health ED visits |
| 14 | Howard | 3082.1 | All races/ ethnicities (aggregated) | 2017 | Mental Health ED visits |
| 15 | Kent | 13662.1 | All races/ ethnicities (aggregated) | 2017 | Mental Health ED visits |
| 16 | Montgomery | 2312.1 | All races/ ethnicities (aggregated) | 2017 | Mental Health ED visits |
| 17 | Prince George's | 1955.6 | All races/ ethnicities (aggregated) | 2017 | Mental Health ED visits |
| 18 | Queen Anne's | 6119.5 | All races/ ethnicities (aggregated) | 2017 | Mental Health ED visits |
| 19 | Saint Mary's | 6173.1 | All races/ ethnicities (aggregated) | 2017 | Mental Health ED visits |
| 20 | Somerset | 2696.1 | All races/ ethnicities (aggregated) | 2017 | Mental Health ED visits |
| 21 | Talbot | 7661.6 | All races/ ethnicities (aggregated) | 2017 | Mental Health ED visits |
| 22 | Washington | 5410.8 | All races/ ethnicities (aggregated) | 2017 | Mental Health ED visits |
| 23 | Wicomico | 2897.6 | All races/ ethnicities (aggregated) | 2017 | Mental Health ED visits |
| 24 | Worcester | 3502.8 | All races/ ethnicities (aggregated) | 2017 | Mental Health ED visits |
In [23]:
ship_df['Jurisdiction'] = ship_df['Jurisdiction'].str.replace("Saint", "St.")In [24]:
ship_df| Jurisdiction | Value | Race/ ethnicity | Year | Measure | |
|---|---|---|---|---|---|
| 1 | Allegany | 3309.6 | All races/ ethnicities (aggregated) | 2017 | Mental Health ED visits |
| 2 | Anne Arundel | 5734.1 | All races/ ethnicities (aggregated) | 2017 | Mental Health ED visits |
| 3 | Baltimore City | 10093.5 | All races/ ethnicities (aggregated) | 2017 | Mental Health ED visits |
| 4 | Baltimore | 4210.1 | All races/ ethnicities (aggregated) | 2017 | Mental Health ED visits |
| 5 | Calvert | 2999.1 | All races/ ethnicities (aggregated) | 2017 | Mental Health ED visits |
| 6 | Caroline | 7556.2 | All races/ ethnicities (aggregated) | 2017 | Mental Health ED visits |
| 7 | Carroll | 4216.0 | All races/ ethnicities (aggregated) | 2017 | Mental Health ED visits |
| 8 | Cecil | 9584.2 | All races/ ethnicities (aggregated) | 2017 | Mental Health ED visits |
| 9 | Charles | 2817.6 | All races/ ethnicities (aggregated) | 2017 | Mental Health ED visits |
| 10 | Dorchester | 11251.8 | All races/ ethnicities (aggregated) | 2017 | Mental Health ED visits |
| 11 | Frederick | 3064.1 | All races/ ethnicities (aggregated) | 2017 | Mental Health ED visits |
| 12 | Garrett | 7967.6 | All races/ ethnicities (aggregated) | 2017 | Mental Health ED visits |
| 13 | Harford | 3020.2 | All races/ ethnicities (aggregated) | 2017 | Mental Health ED visits |
| 14 | Howard | 3082.1 | All races/ ethnicities (aggregated) | 2017 | Mental Health ED visits |
| 15 | Kent | 13662.1 | All races/ ethnicities (aggregated) | 2017 | Mental Health ED visits |
| 16 | Montgomery | 2312.1 | All races/ ethnicities (aggregated) | 2017 | Mental Health ED visits |
| 17 | Prince George's | 1955.6 | All races/ ethnicities (aggregated) | 2017 | Mental Health ED visits |
| 18 | Queen Anne's | 6119.5 | All races/ ethnicities (aggregated) | 2017 | Mental Health ED visits |
| 19 | St. Mary's | 6173.1 | All races/ ethnicities (aggregated) | 2017 | Mental Health ED visits |
| 20 | Somerset | 2696.1 | All races/ ethnicities (aggregated) | 2017 | Mental Health ED visits |
| 21 | Talbot | 7661.6 | All races/ ethnicities (aggregated) | 2017 | Mental Health ED visits |
| 22 | Washington | 5410.8 | All races/ ethnicities (aggregated) | 2017 | Mental Health ED visits |
| 23 | Wicomico | 2897.6 | All races/ ethnicities (aggregated) | 2017 | Mental Health ED visits |
| 24 | Worcester | 3502.8 | All races/ ethnicities (aggregated) | 2017 | Mental Health ED visits |
In [25]:
merged_df = places_df.merge(ship_df, left_on='county', right_on='Jurisdiction', how='left', indicator=False)In [26]:
merged_df| fips | county | pct_lead | Jurisdiction | Value | Race/ ethnicity | Year | Measure | |
|---|---|---|---|---|---|---|---|---|
| 0 | 001 | Allegany | 0.020888 | Allegany | 3309.6 | All races/ ethnicities (aggregated) | 2017 | Mental Health ED visits |
| 1 | 003 | Anne Arundel | 0.003220 | Anne Arundel | 5734.1 | All races/ ethnicities (aggregated) | 2017 | Mental Health ED visits |
| 2 | 005 | Baltimore | 0.009980 | Baltimore | 4210.1 | All races/ ethnicities (aggregated) | 2017 | Mental Health ED visits |
| 3 | 009 | Calvert | 0.006550 | Calvert | 2999.1 | All races/ ethnicities (aggregated) | 2017 | Mental Health ED visits |
| 4 | 011 | Caroline | 0.019973 | Caroline | 7556.2 | All races/ ethnicities (aggregated) | 2017 | Mental Health ED visits |
| 5 | 013 | Carroll | 0.006377 | Carroll | 4216.0 | All races/ ethnicities (aggregated) | 2017 | Mental Health ED visits |
| 6 | 015 | Cecil | 0.006893 | Cecil | 9584.2 | All races/ ethnicities (aggregated) | 2017 | Mental Health ED visits |
| 7 | 017 | Charles | 0.002288 | Charles | 2817.6 | All races/ ethnicities (aggregated) | 2017 | Mental Health ED visits |
| 8 | 019 | Dorchester | 0.027439 | Dorchester | 11251.8 | All races/ ethnicities (aggregated) | 2017 | Mental Health ED visits |
| 9 | 021 | Frederick | 0.003054 | Frederick | 3064.1 | All races/ ethnicities (aggregated) | 2017 | Mental Health ED visits |
| 10 | 023 | Garrett | NaN | Garrett | 7967.6 | All races/ ethnicities (aggregated) | 2017 | Mental Health ED visits |
| 11 | 025 | Harford | 0.003109 | Harford | 3020.2 | All races/ ethnicities (aggregated) | 2017 | Mental Health ED visits |
| 12 | 027 | Howard | 0.008291 | Howard | 3082.1 | All races/ ethnicities (aggregated) | 2017 | Mental Health ED visits |
| 13 | 029 | Kent | NaN | Kent | 13662.1 | All races/ ethnicities (aggregated) | 2017 | Mental Health ED visits |
| 14 | 031 | Montgomery | 0.003826 | Montgomery | 2312.1 | All races/ ethnicities (aggregated) | 2017 | Mental Health ED visits |
| 15 | 033 | Prince George's | 0.012376 | Prince George's | 1955.6 | All races/ ethnicities (aggregated) | 2017 | Mental Health ED visits |
| 16 | 035 | Queen Anne's | NaN | Queen Anne's | 6119.5 | All races/ ethnicities (aggregated) | 2017 | Mental Health ED visits |
| 17 | 037 | St. Mary's | NaN | St. Mary's | 6173.1 | All races/ ethnicities (aggregated) | 2017 | Mental Health ED visits |
| 18 | 039 | Somerset | 0.015837 | Somerset | 2696.1 | All races/ ethnicities (aggregated) | 2017 | Mental Health ED visits |
| 19 | 041 | Talbot | 0.012442 | Talbot | 7661.6 | All races/ ethnicities (aggregated) | 2017 | Mental Health ED visits |
| 20 | 043 | Washington | 0.008881 | Washington | 5410.8 | All races/ ethnicities (aggregated) | 2017 | Mental Health ED visits |
| 21 | 045 | Wicomico | 0.013187 | Wicomico | 2897.6 | All races/ ethnicities (aggregated) | 2017 | Mental Health ED visits |
| 22 | 047 | Worcester | 0.012959 | Worcester | 3502.8 | All races/ ethnicities (aggregated) | 2017 | Mental Health ED visits |
| 23 | 510 | Baltimore City | 0.041661 | Baltimore City | 10093.5 | All races/ ethnicities (aggregated) | 2017 | Mental Health ED visits |
In [27]:
merged_df[['pct_lead','Value']].corr()| pct_lead | Value | |
|---|---|---|
| pct_lead | 1.00000 | 0.59498 |
| Value | 0.59498 | 1.00000 |
Reuben, Aaron, Jonathan D. Schaefer, Terrie E. Moffitt, Jonathan Broadbent, Honalee Harrington, Renate M. Houts, Sandhya Ramrakha, Richie Poulton, and Avshalom Caspi. 2019. “Association of Childhood Lead Exposure With Adult Personality Traits and Lifelong Mental Health.” JAMA Psychiatry 76 (4): 418–25. https://doi.org/10.1001/jamapsychiatry.2018.4192.