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Commit 529b8626 authored by Nicolas Mantilla Molina's avatar Nicolas Mantilla Molina
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Add design final project

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DiseñoExperimentos/Proyecto_Final/Diseño_Trabajo_Final_NMM.ipynb 0 → 100644
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%% Cell type:markdown id: tags:
# Cáncer de cuello uterino en Colombia: Análisis Estadístico
%% Cell type:code id: tags:
``` python
import PyPDF2
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
```
%% Cell type:code id: tags:
``` python
pdf = open('Colombia_profile.pdf', 'rb')
```
%% Cell type:code id: tags:
``` python
pdfReader = PyPDF2.PdfReader(pdf)
bucaramanga = pdfReader.pages[5].extract_text()
cali = pdfReader.pages[13].extract_text()
manizales = pdfReader.pages[21].extract_text()
pasto = pdfReader.pages[29].extract_text()
COL = pdfReader.pages[37].extract_text()
```
%% Cell type:code id: tags:
``` python
pdf.close()
```
%% Cell type:code id: tags:
``` python
col_data = COL.split('\n')[30].split(" ")
buc_data = bucaramanga.split('\n')[30].split(" ")
cal_data = cali.split('\n')[30].split(" ")
man_data = manizales.split('\n')[31].split(" ")
pas_data = pasto.split('\n')[30].split(" ")
```
%% Cell type:code id: tags:
``` python
col_data = [float(i) for i in [col_data[0][5::]] + [col_data[1]] + col_data[7:19]]
buc_data = [float(i) for i in [buc_data[0][5::]] + [buc_data[1]] + buc_data[7:19]]
cal_data = [float(i) for i in [cal_data[0][5::]] + [cal_data[1]] + cal_data[7:19]]
man_data = [float(i) for i in [man_data[0][5::]] + [man_data[1]] + man_data[7:19]]
pas_data = [float(i) for i in [pas_data[0][5::]] + [pas_data[1]] + pas_data[7:19]]
```
%% Cell type:code id: tags:
``` python
df = pd.DataFrame({'Bucaramanga':buc_data,'Cali':cal_data,'Manizales':man_data,'Pasto':pas_data,
'Colombia': col_data}, index=['Total','Desconocido','20-24',
'25-29',' 30-34',' 35-39', '40-44','45-49' ,'50-54', '55-59',
'60-64', '65-69', '70-74', '>75'])
```
%% Cell type:markdown id: tags:
**Incidencia por cada 100.000 mujeres**
%% Cell type:code id: tags:
``` python
df
```
%% Output
Bucaramanga Cali Manizales Pasto Colombia
Total 482.0 1181.0 225.0 271.0 2159.0
Desconocido 4.0 30.0 0.0 0.0 34.0
20-24 7.2 8.1 11.0 8.2 8.2
25-29 21.8 20.3 31.2 20.5 21.7
30-34 25.3 29.9 26.4 34.9 28.9
35-39 26.2 33.0 35.4 39.6 32.1
40-44 35.2 49.1 35.1 71.2 45.9
45-49 35.3 49.4 32.7 69.6 45.7
50-54 42.0 48.3 62.3 71.9 50.5
55-59 60.7 50.3 58.9 77.7 56.6
60-64 52.3 75.5 64.3 110.1 71.4
65-69 69.9 61.4 67.0 101.9 68.1
70-74 56.7 66.2 52.9 94.8 64.6
>75 18.1 21.9 22.7 27.2 21.5
%% Cell type:code id: tags:
``` python
factors = pd.DataFrame({'Ciudad':(['Bucaramanga']*12 +['Cali']*12 + ['Manizales']*12 + ['Pasto']*12 + ['Colombia']*12),
'Edades':('20-24',
'25-29',' 30-34',' 35-39', '40-44','45-49' ,'50-54', '55-59',
'60-64', '65-69', '70-74', '>75')*5, 'Incidencia':
buc_data[2:] + cal_data[2:] + man_data[2:] + pas_data[2:] + col_data[2:]})
factors
```
%% Output
Ciudad Edades Incidencia
0 Bucaramanga 20-24 7.2
1 Bucaramanga 25-29 21.8
2 Bucaramanga 30-34 25.3
3 Bucaramanga 35-39 26.2
4 Bucaramanga 40-44 35.2
5 Bucaramanga 45-49 35.3
6 Bucaramanga 50-54 42.0
7 Bucaramanga 55-59 60.7
8 Bucaramanga 60-64 52.3
9 Bucaramanga 65-69 69.9
10 Bucaramanga 70-74 56.7
11 Bucaramanga >75 18.1
12 Cali 20-24 8.1
13 Cali 25-29 20.3
14 Cali 30-34 29.9
15 Cali 35-39 33.0
16 Cali 40-44 49.1
17 Cali 45-49 49.4
18 Cali 50-54 48.3
19 Cali 55-59 50.3
20 Cali 60-64 75.5
21 Cali 65-69 61.4
22 Cali 70-74 66.2
23 Cali >75 21.9
24 Manizales 20-24 11.0
25 Manizales 25-29 31.2
26 Manizales 30-34 26.4
27 Manizales 35-39 35.4
28 Manizales 40-44 35.1
29 Manizales 45-49 32.7
30 Manizales 50-54 62.3
31 Manizales 55-59 58.9
32 Manizales 60-64 64.3
33 Manizales 65-69 67.0
34 Manizales 70-74 52.9
35 Manizales >75 22.7
36 Pasto 20-24 8.2
37 Pasto 25-29 20.5
38 Pasto 30-34 34.9
39 Pasto 35-39 39.6
40 Pasto 40-44 71.2
41 Pasto 45-49 69.6
42 Pasto 50-54 71.9
43 Pasto 55-59 77.7
44 Pasto 60-64 110.1
45 Pasto 65-69 101.9
46 Pasto 70-74 94.8
47 Pasto >75 27.2
48 Colombia 20-24 8.2
49 Colombia 25-29 21.7
50 Colombia 30-34 28.9
51 Colombia 35-39 32.1
52 Colombia 40-44 45.9
53 Colombia 45-49 45.7
54 Colombia 50-54 50.5
55 Colombia 55-59 56.6
56 Colombia 60-64 71.4
57 Colombia 65-69 68.1
58 Colombia 70-74 64.6
59 Colombia >75 21.5
%% Cell type:code id: tags:
``` python
fig, axs = plt.subplots(2, 1, figsize=(20, 20))
axs[0].set_title('Región vs Incidencia',fontsize=15)
sns.boxplot(x="Incidencia", y="Ciudad", data=factors, ax=axs[0])
sns.swarmplot(x="Incidencia", y="Ciudad", data=factors, color='black',
alpha = 0.5, ax=axs[0])
axs[1].set_title('Edad vs Incidencia')
sns.boxplot(x="Edades", y="Incidencia", data=factors, ax=axs[1])
sns.swarmplot(x="Edades", y="Incidencia", data=factors, color='black',alpha = 0.5, ax=axs[1])
```
%% Output
<Axes: title={'center': 'Edad vs Incidencia'}, xlabel='Edades', ylabel='Incidencia'>
%% Cell type:code id: tags:
``` python
plt.figure(figsize=(10, 8))
sns.displot(data=factors, bins=10, x="Incidencia", col="Ciudad", col_wrap=2, kde=True, hue="Ciudad",
palette="dark", legend=False)
```
%% Output
<seaborn.axisgrid.FacetGrid at 0x1aab9853640>
%% Cell type:code id: tags:
``` python
```
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