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US Mortality - Race and Manner of Death

Dr. Shahin Rostami

2020-06-09

10 min read

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Contents Chat Share Follow Download Source

Preamble
Introduction

The Dataset

Data Wrangling
Chord Diagram
Chord Diagram with Names
Conclusion

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Below is a featured selection from this section. You can access this notebook and more by getting the e-book, Data is Beautiful.

Chord Diagram

Preamble¶

In [1]:

import numpy as np                   # for multi-dimensional containers 
import pandas as pd                  # for DataFrames
import itertools
from chord import Chord

Introduction¶

In previous sections, we visualised co-occurrences of Pokémon type. Whilst it was interesting to look at, the dataset only contained Pokémon from the first six geerations. In this section, we're going to use the TidyTuesday Animal Crossing villagers dataset to visualise the relationship between Species and .

The Dataset¶

The dataset documentation states that we can expect 13 variables per each of the 1017 Pokémon of the first eight generations.

Let's download the mirrored dataset and have a look for ourselves.

In [2]:

data_url = '/Users/shahin/Documents/devel/data/2015_data.csv'
data = pd.read_csv(data_url)
data.head()

/Users/shahin/opt/miniconda3/envs/analytics/lib/python3.7/site-packages/IPython/core/interactiveshell.py:3063: DtypeWarning: Columns (40,41,42,43,61,62,63,64) have mixed types.Specify dtype option on import or set low_memory=False.
  interactivity=interactivity, compiler=compiler, result=result)

Out[2]:

	resident_status	education_1989_revision	education_2003_revision	education_reporting_flag	month_of_death	sex	detail_age_type	detail_age	age_substitution_flag	age_recode_52	...	record_condition_18	record_condition_19	record_condition_20	race	bridged_race_flag	race_imputation_flag	race_recode_3	race_recode_5	hispanic_origin	hispanic_originrace_recode
0	1	NaN	3.0	1	1	M	1	84	NaN	42	...	NaN	NaN	NaN	1	NaN	NaN	1	1	100	6
1	1	NaN	6.0	1	1	M	1	70	NaN	40	...	NaN	NaN	NaN	1	NaN	NaN	1	1	100	6
2	1	NaN	3.0	1	1	F	1	91	NaN	44	...	NaN	NaN	NaN	1	NaN	NaN	1	1	100	6
3	1	NaN	3.0	1	1	F	1	40	NaN	34	...	NaN	NaN	NaN	3	NaN	NaN	2	3	100	8
4	1	NaN	5.0	1	1	F	1	89	NaN	43	...	NaN	NaN	NaN	1	NaN	NaN	1	1	100	6

5 rows × 77 columns

In [3]:

data['race_recode_5'].value_counts()

Out[3]:

1    2311103
2     320759
4      67295
3      19041
Name: race_recode_5, dtype: int64

In [4]:

data.columns

Out[4]:

Index(['resident_status', 'education_1989_revision', 'education_2003_revision',
       'education_reporting_flag', 'month_of_death', 'sex', 'detail_age_type',
       'detail_age', 'age_substitution_flag', 'age_recode_52', 'age_recode_27',
       'age_recode_12', 'infant_age_recode_22',
       'place_of_death_and_decedents_status', 'marital_status',
       'day_of_week_of_death', 'current_data_year', 'injury_at_work',
       'manner_of_death', 'method_of_disposition', 'autopsy', 'activity_code',
       'place_of_injury_for_causes_w00_y34_except_y06_and_y07_',
       'icd_code_10th_revision', '358_cause_recode', '113_cause_recode',
       '130_infant_cause_recode', '39_cause_recode',
       'number_of_entity_axis_conditions', 'entity_condition_1',
       'entity_condition_2', 'entity_condition_3', 'entity_condition_4',
       'entity_condition_5', 'entity_condition_6', 'entity_condition_7',
       'entity_condition_8', 'entity_condition_9', 'entity_condition_10',
       'entity_condition_11', 'entity_condition_12', 'entity_condition_13',
       'entity_condition_14', 'entity_condition_15', 'entity_condition_16',
       'entity_condition_17', 'entity_condition_18', 'entity_condition_19',
       'entity_condition_20', 'number_of_record_axis_conditions',
       'record_condition_1', 'record_condition_2', 'record_condition_3',
       'record_condition_4', 'record_condition_5', 'record_condition_6',
       'record_condition_7', 'record_condition_8', 'record_condition_9',
       'record_condition_10', 'record_condition_11', 'record_condition_12',
       'record_condition_13', 'record_condition_14', 'record_condition_15',
       'record_condition_16', 'record_condition_17', 'record_condition_18',
       'record_condition_19', 'record_condition_20', 'race',
       'bridged_race_flag', 'race_imputation_flag', 'race_recode_3',
       'race_recode_5', 'hispanic_origin', 'hispanic_originrace_recode'],
      dtype='object')

capitalise the name, personality, and species of each villager.

In [5]:

#data['manner'] = data['manner_of_death']#.str.capitalize()
#data['race_recode_5'] = data['race_recode_5']#.str.capitalize()
#data['species'] = data['species'].str.capitalize()

It looks good so far, but let's confirm the 13 variables against 1017 samples from the documentation.

In [6]:

data.shape

Out[6]:

(2718198, 77)

Perfect, that's exactly what we were expecting.

Data Wrangling¶

We need to do a bit of data wrangling before we can visualise our data. We can see from the columns names that the Pokémon types are split between the columns Type 1 and Type 2.

In [7]:

pd.DataFrame(data.columns.values.tolist())

Out[7]:

	0
0	resident_status
1	education_1989_revision
2	education_2003_revision
3	education_reporting_flag
4	month_of_death
...	...
72	race_imputation_flag
73	race_recode_3
74	race_recode_5
75	hispanic_origin
76	hispanic_originrace_recode

77 rows × 1 columns

So let's select just these two columns and work with a list containing only them as we move forward.

In [8]:

data.fillna(0, inplace=True)

In [ ]:

In [9]:

data.iloc[6572].manner_of_death

Out[9]:

7.0

In [11]:

data.manner_of_death.value_counts()

Out[11]:

7.0    2107352
0.0     388364
1.0     143961
2.0      44417
3.0      18885
5.0      11054
4.0       4165
Name: manner_of_death, dtype: int64

In [ ]:

{'143961': 'Accident',
 '44417': 'Suicide',
 '18885': 'Homicide',
 '4165': 'Pending investigation',
 '11054': 'Could not determine',
 '2107352': 'Natural',
 '388364': 'Not specified'}

In [10]:

import json

In [11]:

with open("/Users/shahin/Documents/devel/data/2015_data.json", "r") as read_file:
    codes = json.load(read_file)

In [12]:

codes['manner_of_death']

Out[12]:

{'1': 'Accident',
 '2': 'Suicide',
 '3': 'Homicide',
 '4': 'Pending investigation',
 '5': 'Could not determine',
 '7': 'Natural',
 'Blank': 'Not specified'}

In [13]:

codes['manner_of_death']['0'] = codes['manner_of_death'].pop('Blank')

In [14]:

remove = ["Natural", "Not specified", "Could not determine", "Pending investigation"]

In [15]:

list(codes['manner_of_death'].values())

Out[15]:

['Accident',
 'Suicide',
 'Homicide',
 'Pending investigation',
 'Could not determine',
 'Natural',
 'Not specified']

In [16]:

left = list(codes['race_recode_5'].values())
pd.DataFrame(left)

Out[16]:

	0
0	White
1	Black
2	American Indian
3	Asian or Pacific Islander

In [17]:

right = list(codes['manner_of_death'].values())
pd.DataFrame(right)

Out[17]:

	0
0	Accident
1	Suicide
2	Homicide
3	Pending investigation
4	Could not determine
5	Natural
6	Not specified

In [18]:

right = [x for x in right if x not in remove]

In [19]:

data['manner_of_death'] = data['manner_of_death'].astype('int32')
data['manner_of_death'] = data['manner_of_death'].astype('str')

data.iloc[6572].manner_of_death

Out[19]:

'7'

In [20]:

left.sort()
right.sort()

In [21]:

left

Out[21]:

['American Indian', 'Asian or Pacific Islander', 'Black', 'White']

In [22]:

data = data.replace({"manner_of_death": codes['manner_of_death']})

In [23]:

data['race_recode_5'] = data['race_recode_5'].astype('int32')
data['race_recode_5'] = data['race_recode_5'].astype('str')

data.iloc[6572].race_recode_5

Out[23]:

'2'

In [24]:

data = data.replace({"race_recode_5": codes['race_recode_5']})

In [25]:

data.iloc[6572].race_recode_5

Out[25]:

'Black'

In [26]:

manner_race = pd.DataFrame(data[['manner_of_death', 'race_recode_5']].values)
manner_race

Out[26]:

	0	1
0	Natural	White
1	Natural	White
2	Natural	White
3	Homicide	American Indian
4	Natural	White
...	...	...
2718193	Natural	Black
2718194	Natural	White
2718195	Natural	White
2718196	Natural	Black
2718197	Natural	Black

2718198 rows × 2 columns

Now for the names of our types.

Which we can now use to create the matrix.

In [27]:

features= left+right
d = pd.DataFrame(0, index=features, columns=features)

Our chord diagram will need two inputs: the co-occurrence matrix, and a list of names to label the segments.

We can build a co-occurrence matrix with the following approach. We'll start by creating a list with every type pairing in its original and reversed form.

In [28]:

manner_race = list(itertools.chain.from_iterable((i, i[::-1]) for i in manner_race.values))

In [29]:

for x in manner_race:
    if(x[0] not in remove and x[1] not in remove):
        d.at[x[0], x[1]] += 1

In [30]:

Out[30]:

	American Indian	Asian or Pacific Islander	Black	White	Accident	Homicide	Suicide
American Indian	0	0	0	0	2067	304	582
Asian or Pacific Islander	0	0	0	0	3032	364	1334
Black	0	0	0	0	15746	9419	2518
White	0	0	0	0	123116	8798	39983
Accident	2067	3032	15746	123116	0	0	0
Homicide	304	364	9419	8798	0	0	0
Suicide	582	1334	2518	39983	0	0	0

In [31]:

for race in left:
    d.loc[ race , : ] = ((d.loc[ race , : ] / d.loc[ race , : ].sum()) * 100)
    d.loc[ : , race  ] = ((d.loc[  : , race  ] / d.loc[  : , race  ].sum()) * 100)

In [32]:

(d[race].value_counts(normalize=True)*100).astype(int)

Out[32]:

0.000000     57
71.621960    14
23.259859    14
5.118181     14
Name: White, dtype: int64

In [33]:

Out[33]:

	American Indian	Asian or Pacific Islander	Black	White	Accident	Homicide	Suicide
American Indian	0.000000	0.00000	0.000000	0.000000	69.996614	10.294616	19.708771
Asian or Pacific Islander	0.000000	0.00000	0.000000	0.000000	64.101480	7.695560	28.202960
Black	0.000000	0.00000	0.000000	0.000000	56.879673	34.024492	9.095835
White	0.000000	0.00000	0.000000	0.000000	71.621960	5.118181	23.259859
Accident	69.996614	64.10148	56.879673	71.621960	0.000000	0.000000	0.000000
Homicide	10.294616	7.69556	34.024492	5.118181	0.000000	0.000000	0.000000
Suicide	19.708771	28.20296	9.095835	23.259859	0.000000	0.000000	0.000000

Chord Diagram¶

Time to visualise the co-occurrence of types using a chord diagram. We are going to use a list of custom colours that represent the types.

In [34]:

colors = ["#2DE1FC","#1883B4","#C5DB66","#90B64D","#DB2B39","#E76926", "#DB9118"]

In [35]:

names = left + right

Finally, we can put it all together.

In [ ]:

In [36]:

names[1]= "Asian or PI"


names[0]= "AIAN"

In [37]:

Out[37]:

	American Indian	Asian or Pacific Islander	Black	White	Accident	Homicide	Suicide
American Indian	0.000000	0.00000	0.000000	0.000000	69.996614	10.294616	19.708771
Asian or Pacific Islander	0.000000	0.00000	0.000000	0.000000	64.101480	7.695560	28.202960
Black	0.000000	0.00000	0.000000	0.000000	56.879673	34.024492	9.095835
White	0.000000	0.00000	0.000000	0.000000	71.621960	5.118181	23.259859
Accident	69.996614	64.10148	56.879673	71.621960	0.000000	0.000000	0.000000
Homicide	10.294616	7.69556	34.024492	5.118181	0.000000	0.000000	0.000000
Suicide	19.708771	28.20296	9.095835	23.259859	0.000000	0.000000	0.000000

Finally, we can put it all together but this time with the details matrix passed in.

In [38]:

Chord(
    d.round(2).values.tolist(),
    names,
    colors=colors,
    credit=True,
      wrap_labels=True,
      margin=50, 
    font_size_large=7,
divide=True,
    noun="percent",
    divide_idx=len(left),
    divide_size=.2,
    width=850).show()

Chord Diagram

Chord Diagram with Names¶

It would be nice to show a list of Pokémon names when hovering over co-occurring Pokémon types. To do this, we can make use of the optional details parameter.

In [42]:

Out[42]:

	American Indian	Asian or Pacific Islander	Black	White	Accident	Homicide	Suicide
American Indian	0.000000	0.00000	0.000000	0.000000	69.996614	10.294616	19.708771
Asian or Pacific Islander	0.000000	0.00000	0.000000	0.000000	64.101480	7.695560	28.202960
Black	0.000000	0.00000	0.000000	0.000000	56.879673	34.024492	9.095835
White	0.000000	0.00000	0.000000	0.000000	71.621960	5.118181	23.259859
Accident	69.996614	64.10148	56.879673	71.621960	0.000000	0.000000	0.000000
Homicide	10.294616	7.69556	34.024492	5.118181	0.000000	0.000000	0.000000
Suicide	19.708771	28.20296	9.095835	23.259859	0.000000	0.000000	0.000000

Next, we'll create an empty multi-dimensional array with the same shape as our matrix.

In [39]:

details = np.empty((len(names),len(names)),dtype=object)
details_thumbs = np.empty((len(names),len(names)),dtype=object)

Now we can populate the details array with lists of Pokémon names in the correct positions.

In [40]:

for count_x, item_x in enumerate(names):
    for count_y, item_y in enumerate(names):
        details_urls = data[
            (data['species'].isin([item_x, item_y])) &
            (data['personality'].isin([item_y, item_x]))]['url'].to_list()
        
        details_names = data[
            (data['species'].isin([item_x, item_y])) &
            (data['personality'].isin([item_y, item_x]))]['name'].to_list()
        
        urls_names = np.column_stack((details_urls, details_names))
        if(urls_names.size > 0):
            details[count_x][count_y] = details_names
            details_thumbs[count_x][count_y] = details_urls

        else:
            details[count_x][count_y] = []
            details_thumbs[count_x][count_y] = []

details=pd.DataFrame(details).values.tolist()
details_thumbs=pd.DataFrame(details_thumbs).values.tolist()

---------------------------------------------------------------------------
KeyError                                  Traceback (most recent call last)
~/opt/miniconda3/envs/analytics/lib/python3.7/site-packages/pandas/core/indexes/base.py in get_loc(self, key, method, tolerance)
   2645             try:
-> 2646                 return self._engine.get_loc(key)
   2647             except KeyError:

pandas/_libs/index.pyx in pandas._libs.index.IndexEngine.get_loc()

pandas/_libs/index.pyx in pandas._libs.index.IndexEngine.get_loc()

pandas/_libs/hashtable_class_helper.pxi in pandas._libs.hashtable.PyObjectHashTable.get_item()

pandas/_libs/hashtable_class_helper.pxi in pandas._libs.hashtable.PyObjectHashTable.get_item()

KeyError: 'species'

During handling of the above exception, another exception occurred:

KeyError                                  Traceback (most recent call last)
<ipython-input-40-f54e3a0b2906> in <module>
      2     for count_y, item_y in enumerate(names):
      3         details_urls = data[
----> 4             (data['species'].isin([item_x, item_y])) &
      5             (data['personality'].isin([item_y, item_x]))]['url'].to_list()
      6 

~/opt/miniconda3/envs/analytics/lib/python3.7/site-packages/pandas/core/frame.py in __getitem__(self, key)
   2798             if self.columns.nlevels > 1:
   2799                 return self._getitem_multilevel(key)
-> 2800             indexer = self.columns.get_loc(key)
   2801             if is_integer(indexer):
   2802                 indexer = [indexer]

~/opt/miniconda3/envs/analytics/lib/python3.7/site-packages/pandas/core/indexes/base.py in get_loc(self, key, method, tolerance)
   2646                 return self._engine.get_loc(key)
   2647             except KeyError:
-> 2648                 return self._engine.get_loc(self._maybe_cast_indexer(key))
   2649         indexer = self.get_indexer([key], method=method, tolerance=tolerance)
   2650         if indexer.ndim > 1 or indexer.size > 1:

pandas/_libs/index.pyx in pandas._libs.index.IndexEngine.get_loc()

pandas/_libs/index.pyx in pandas._libs.index.IndexEngine.get_loc()

pandas/_libs/hashtable_class_helper.pxi in pandas._libs.hashtable.PyObjectHashTable.get_item()

pandas/_libs/hashtable_class_helper.pxi in pandas._libs.hashtable.PyObjectHashTable.get_item()

KeyError: 'species'

In [ ]:

len(right)

In [ ]:

Chord(d.values.tolist(), names,credit=True, colors=colors, wrap_labels=False,
      margin=40, font_size_large=7,details=details,details_thumbs=details_thumbs,noun="villagers",
        details_separator="", divide=True, divide_idx=len(left),divide_size=.2, width=850).show()

In [ ]:

np.empty(shape=(6,1)).tolist()

Conclusion¶

In this section, we demonstrated how to conduct some data wrangling on a downloaded dataset to prepare it for a chord diagram. Our chord diagram is interactive, so you can use your mouse or touchscreen to investigate the co-occurrences!

Get access to this section and more

You can access this notebook and more by getting the e-book, Data is Beautiful.

Data is Beautiful

A practical book on data visualisation that shows you how to create static and interactive visualisations that are engaging and beautiful.

Get the book

	resident_status	education_1989_revision	education_2003_revision	education_reporting_flag	month_of_death	sex	detail_age_type	detail_age	age_substitution_flag	age_recode_52	...	record_condition_18	record_condition_19	record_condition_20	race	bridged_race_flag	race_imputation_flag	race_recode_3	race_recode_5	hispanic_origin	hispanic_originrace_recode
0	1	NaN	3.0	1	1	M	1	84	NaN	42	...	NaN	NaN	NaN	1	NaN	NaN	1	1	100	6
1	1	NaN	6.0	1	1	M	1	70	NaN	40	...	NaN	NaN	NaN	1	NaN	NaN	1	1	100	6
2	1	NaN	3.0	1	1	F	1	91	NaN	44	...	NaN	NaN	NaN	1	NaN	NaN	1	1	100	6
3	1	NaN	3.0	1	1	F	1	40	NaN	34	...	NaN	NaN	NaN	3	NaN	NaN	2	3	100	8
4	1	NaN	5.0	1	1	F	1	89	NaN	43	...	NaN	NaN	NaN	1	NaN	NaN	1	1	100	6

	resident_status	education_1989_revision	education_2003_revision	education_reporting_flag	month_of_death	sex	detail_age_type	detail_age	age_substitution_flag	age_recode_52	...	record_condition_18	record_condition_19	record_condition_20	race	bridged_race_flag	race_imputation_flag	race_recode_3	race_recode_5	hispanic_origin	hispanic_originrace_recode
0	1	NaN	3.0	1	1	M	1	84	NaN	42	...	NaN	NaN	NaN	1	NaN	NaN	1	1	100	6
1	1	NaN	6.0	1	1	M	1	70	NaN	40	...	NaN	NaN	NaN	1	NaN	NaN	1	1	100	6
2	1	NaN	3.0	1	1	F	1	91	NaN	44	...	NaN	NaN	NaN	1	NaN	NaN	1	1	100	6
3	1	NaN	3.0	1	1	F	1	40	NaN	34	...	NaN	NaN	NaN	3	NaN	NaN	2	3	100	8
4	1	NaN	5.0	1	1	F	1	89	NaN	43	...	NaN	NaN	NaN	1	NaN	NaN	1	1	100	6

	resident_status	education_1989_revision	education_2003_revision	education_reporting_flag	month_of_death	sex	detail_age_type	detail_age	age_substitution_flag	age_recode_52	...	record_condition_18	record_condition_19	record_condition_20	race	bridged_race_flag	race_imputation_flag	race_recode_3	race_recode_5	hispanic_origin	hispanic_originrace_recode
0	1	NaN	3.0	1	1	M	1	84	NaN	42	...	NaN	NaN	NaN	1	NaN	NaN	1	1	100	6
1	1	NaN	6.0	1	1	M	1	70	NaN	40	...	NaN	NaN	NaN	1	NaN	NaN	1	1	100	6
2	1	NaN	3.0	1	1	F	1	91	NaN	44	...	NaN	NaN	NaN	1	NaN	NaN	1	1	100	6
3	1	NaN	3.0	1	1	F	1	40	NaN	34	...	NaN	NaN	NaN	3	NaN	NaN	2	3	100	8
4	1	NaN	5.0	1	1	F	1	89	NaN	43	...	NaN	NaN	NaN	1	NaN	NaN	1	1	100	6