DataSUS Vaccines Pipeline Cleansing

import pandas as pd
from os import listdir

datasets = sorted([x for x in listdir('./datasets')])
datasets

['cv_ano_imuno.csv',
 'cv_uf_2015.csv',
 'cv_uf_2016.csv',
 'cv_uf_2017.csv',
 'cv_uf_2018.csv',
 'cv_uf_2019.csv']

def filter_df(data, iterator, year=None):
    df = data[iterator]
    df.columns = df.iloc[0]
    df = df.iloc[1:]
    df = df.reset_index()
    df = df.rename(columns={"index":"Estado"})
    if year is not None:
        df['Ano'] = year

    return df

filepath = './datasets/'

df = [pd.read_csv(filepath + dataset, encoding='utf8', sep=';', decimal=',') for dataset in datasets]

df_t = [x.transpose() for x in df]

imuno = filter_df(df_t, 0)
imuno = imuno.rename(columns={"Estado": "Ano"})
df_2015 = filter_df(df_t, 1, 2015)
df_2016 = filter_df(df_t, 2, 2016)
df_2017 = filter_df(df_t, 3, 2017)
df_2018 = filter_df(df_t, 4, 2018)
df_2019 = filter_df(df_t, 5, 2019)

df_2015.head(2)

Imuno	Estado	BCG	Hepatite B em crianças até 30 dias	Rotavírus Humano	Meningococo C	Hepatite B	Penta	Pneumocócica	Poliomielite	Febre Amarela	...	Tríplice Viral D2	Tetra Viral(SRC+VZ)	DTP	Tríplice Bacteriana(DTP)(1º ref)	Dupla adulto e tríplice acelular gestante	dTpa gestante	Tetravalente (DTP/Hib) (TETRA)	Ignorado	Total	Ano
0	RO	110.77	104.78	103.95	104.02	106.35	104.54	104.68	105.44	106.06	...	94.61	94.63	104.63	95.82	73.92	64.65	103.36	229.39	111.27	2015
1	AC	105.9	69.63	82.5	88.64	82.62	81.24	72.48	82.74	66.67	...	51.69	49.3	81.3	62.7	17.13	12.08	83.71	158.09	75.54	2015

2 rows × 25 columns

bases = [df_2015, df_2016, df_2017, df_2018, df_2019]
result = pd.concat(bases)
result.columns

Index(['Estado', 'BCG', 'Hepatite B  em crianças até 30 dias',
       'Rotavírus Humano', 'Meningococo C', 'Hepatite B', 'Penta',
       'Pneumocócica', 'Poliomielite', 'Febre Amarela', 'Hepatite A',
       'Pneumocócica(1º ref)', 'Meningococo C (1º ref)',
       'Poliomielite(1º ref)', 'Tríplice Viral  D1', 'Tríplice Viral  D2',
       'Tetra Viral(SRC+VZ)', 'DTP', 'Tríplice Bacteriana(DTP)(1º ref)',
       'Dupla adulto e tríplice acelular gestante', 'dTpa gestante',
       'Tetravalente (DTP/Hib) (TETRA)', 'Ignorado', 'Total', 'Ano',
       'DTP REF (4 e 6 anos)', 'Poliomielite 4 anos'],
      dtype='object')

result.groupby(['Estado', 'Ano']).sum()

		BCG	Hepatite B em crianças até 30 dias	Rotavírus Humano	Meningococo C	Hepatite B	Penta	Pneumocócica	Poliomielite	Febre Amarela	Hepatite A	...	Tetra Viral(SRC+VZ)	DTP	Tríplice Bacteriana(DTP)(1º ref)	Dupla adulto e tríplice acelular gestante	dTpa gestante	Tetravalente (DTP/Hib) (TETRA)	Ignorado	Total	DTP REF (4 e 6 anos)	Poliomielite 4 anos
Estado	Ano
Total	2015	105.08	90.93	95.35	98.19	97.74	96.30	94.23	98.29	46.31	97.07	...	77.37	96.90	85.78	45.57	44.97	95.49	196.58	95.07	0.00	0.00
	2016	95.55	81.75	88.98	91.68	105.19	89.27	95.00	84.43	44.59	71.58	...	79.04	89.53	64.28	31.53	33.81	5.21	16.44	50.44	2.73	0.00
	2017	97.98	85.88	85.12	87.44	84.40	84.24	92.15	84.74	47.37	78.94	...	35.44	0.00	72.40	34.73	42.40	0.00	0.00	72.93	66.08	62.26
	2018	99.72	88.40	91.33	88.49	88.53	88.49	95.25	89.54	59.50	82.69	...	33.26	0.00	73.27	44.99	60.23	0.00	0.00	77.13	68.52	63.62
	2019	86.67	78.57	85.40	87.41	70.77	70.76	89.07	84.19	62.41	85.02	...	34.24	0.00	57.08	45.02	63.23	0.00	0.00	73.44	53.74	68.45
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
TO	2015	107.48	94.35	97.73	98.73	100.00	98.70	93.92	97.22	83.28	94.52	...	52.17	98.76	83.28	15.24	16.55	96.74	231.40	92.37	0.00	0.00
	2016	99.23	77.44	91.00	92.43	106.61	88.14	97.31	84.77	77.26	75.63	...	88.32	88.14	69.89	11.52	19.23	5.49	31.04	60.94	2.28	0.00
	2017	104.75	91.32	89.09	90.01	85.77	85.76	93.17	86.05	73.28	81.50	...	60.91	0.00	75.43	34.82	53.33	0.00	0.00	77.95	72.35	66.53
	2018	104.06	100.67	93.40	79.72	90.04	90.04	100.71	91.68	81.08	84.08	...	65.92	0.00	74.54	45.80	70.06	0.00	0.00	81.12	65.28	62.70
	2019	112.41	111.88	88.73	93.52	76.38	76.38	94.02	88.17	76.47	87.09	...	81.35	0.00	55.40	51.01	75.97	0.00	0.00	82.12	53.13	65.91

140 rows × 25 columns

result.to_csv('cv_uf.csv', encoding='utf8', sep=',', decimal='.', index=False)

# !jupyter nbconvert main.ipynb --to markdown --output README.md

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