Metodo local¶

In [12]:

from google.colab import files
uploaded = files.upload()
import io
df2 = pd.read_csv(io.BytesIO(uploaded['dnrpa-robos-recuperos-autos-202201.csv']),sep=",")
df2.head(10)

Upload widget is only available when the cell has been executed in the current browser session. Please rerun this cell to enable.

Saving dnrpa-robos-recuperos-autos-202201.csv to dnrpa-robos-recuperos-autos-202201.csv

Out[12]:

	tramite_tipo	tramite_fecha	fecha_inscripcion_inicial	registro_seccional_codigo	registro_seccional_descripcion	registro_seccional_provincia	automotor_origen	automotor_anio_modelo	automotor_tipo_codigo	automotor_tipo_descripcion	automotor_marca_codigo	automotor_marca_descripcion	automotor_modelo_codigo	automotor_modelo_descripcion	automotor_uso_codigo	automotor_uso_descripcion	titular_tipo_persona	titular_domicilio_localidad	titular_domicilio_provincia	titular_genero	titular_anio_nacimiento	titular_pais_nacimiento	titular_porcentaje_titularidad	titular_domicilio_provincia_id	titular_pais_nacimiento_id
0	DENUNCIA DE ROBO O HURTO / RETENCION INDEBIDA	2022-01-18	1994-06-03	1173	BERAZATEGUI Nº 2	Buenos Aires	Nacional	1994.0	NaN	FURGON VIDRID.C/ASIE	37	RENAULT	34	RENAULT TRAFIC	1	Privado	Física	BERAZATEGUI	BUENOS AIRES	Masculino	1972	ARGENTINA	100	6	ARG
1	DENUNCIA DE ROBO O HURTO / RETENCION INDEBIDA	2022-01-31	2000-10-27	1120	LA MATANZA Nº 03	Buenos Aires	Nacional	2000.0	NaN	SEDAN	08	CHEVROLET	84	CORSA 3P CITI 1.0 N	1	Privado	Física	LOMAS DEL MIRADOR	BUENOS AIRES	Femenino	1991	ARGENTINA	100	6	ARG
2	DENUNCIA DE ROBO O HURTO / RETENCION INDEBIDA	2022-01-11	1994-04-28	2086	CAPITAL FEDERAL Nº 086	Ciudad Autónoma de Bs.As.	Nacional	1994.0	NaN	FUR.VIDRIADO C/ASIEN	37	RENAULT	55	RENAULT TRAFIC	1	Privado	Física	C.AUTONOMA DE BS.AS	C.AUTONOMA DE BS.AS	Masculino	1971	PERU	100	2	NaN
3	DENUNCIA DE ROBO O HURTO / RETENCION INDEBIDA	2022-01-10	1992-03-27	1119	MORON Nº 03	Buenos Aires	Importado	1991.0	NaN	RURAL 4 PTAS.	NaN	NISSAN	NaN	PATHFINDER SE-V6	1	Privado	Física	HAEDO	BUENOS AIRES	No identificado	1982	ARGENTINA	50	6	ARG
4	DENUNCIA DE ROBO O HURTO / RETENCION INDEBIDA	2022-01-24	2000-11-01	1118	QUILMES Nº 2	Buenos Aires	Nacional	2000.0	NaN	SEDAN 4 PUERTAS	08	CHEVROLET	57	CORSA 4 PUERTAS WIND 1.6 MPFI	1	Privado	Física	QUILMES OESTE	BUENOS AIRES	Masculino	1947	ARGENTINA	100	6	ARG
5	DENUNCIA DE ROBO O HURTO / RETENCION INDEBIDA	2022-01-03	1995-11-10	4058	EMBALSE	Córdoba	Nacional	1995.0	NaN	CHASIS C/CABINA P/CA	29	MERCEDES BENZ	55	BM386 VERSION 1633	1	Privado	Física	VILLA RUMIPAL	CORDOBA	Masculino	1980	ARGENTINA	100	14	ARG
6	DENUNCIA DE ROBO O HURTO / RETENCION INDEBIDA	2022-01-12	2000-04-05	1113	SAN VICENTE	Buenos Aires	Nacional	2000.0	NaN	3 PTAS	08	CHEVROLET	77	CORSA 3 DOOR WIND 1.6 MPFI	1	Privado	Física	GUERNICA	BUENOS AIRES	Femenino	1978	PARAGUAY	100	6	PRY
7	DENUNCIA DE ROBO O HURTO / RETENCION INDEBIDA	2022-01-18	1997-11-18	6004	MACHAGAI	Chaco	Protocolo 21	1997.0	NaN	FURGON VIDRIADO C/ ASIENTOS	112	RENAULT	122	EXPRESS	1	Privado	Física	COLONIAS UNIDAS	CHACO	Masculino	1976	ARGENTINA	100	22	ARG
8	DENUNCIA DE ROBO O HURTO / RETENCION INDEBIDA	2022-01-06	2014-06-09	2045	CAPITAL FEDERAL Nº 045	Ciudad Autónoma de Bs.As.	Protocolo 21	2014.0	NaN	TRANSPORTE DE CARGA	092	MERCEDES BENZ	272	710	1	Privado	Jurídica	C.AUTONOMA DE BS.AS	C.AUTONOMA DE BS.AS	No aplica	1994	No aplica	100	2	NaN
9	DENUNCIA DE ROBO O HURTO / RETENCION INDEBIDA	2022-01-28	1998-06-12	2078	CAPITAL FEDERAL Nº 078	Ciudad Autónoma de Bs.As.	Nacional	1998.0	NaN	BERLINA 5 PTAS.	37	RENAULT	CD	CLIO DIESEL 5P DA AA	1	Privado	Física	C.AUTONOMA DE BS.AS	C.AUTONOMA DE BS.AS	No identificado	1978	ARGENTINA	100	2	ARG

Metodo drive¶

In [51]:

from google.colab import drive
import os
import pandas as pd
drive.mount('/content/gdrive')
%cd '/content/gdrive/MyDrive/'
df2 = pd.read_csv('dnrpa-robos-recuperos-autos-202201.csv',sep=",")
df2.head(10)

Drive already mounted at /content/gdrive; to attempt to forcibly remount, call drive.mount("/content/gdrive", force_remount=True).
/content/gdrive/MyDrive

Out[51]:

	tramite_tipo	tramite_fecha	fecha_inscripcion_inicial	registro_seccional_codigo	registro_seccional_descripcion	registro_seccional_provincia	automotor_origen	automotor_anio_modelo	automotor_tipo_codigo	automotor_tipo_descripcion	automotor_marca_codigo	automotor_marca_descripcion	automotor_modelo_codigo	automotor_modelo_descripcion	automotor_uso_codigo	automotor_uso_descripcion	titular_tipo_persona	titular_domicilio_localidad	titular_domicilio_provincia	titular_genero	titular_anio_nacimiento	titular_pais_nacimiento	titular_porcentaje_titularidad	titular_domicilio_provincia_id	titular_pais_nacimiento_id
0	DENUNCIA DE ROBO O HURTO / RETENCION INDEBIDA	2022-01-18	1994-06-03	1173	BERAZATEGUI Nº 2	Buenos Aires	Nacional	1994.0	NaN	FURGON VIDRID.C/ASIE	37	RENAULT	34	RENAULT TRAFIC	1	Privado	Física	BERAZATEGUI	BUENOS AIRES	Masculino	1972	ARGENTINA	100	6	ARG
1	DENUNCIA DE ROBO O HURTO / RETENCION INDEBIDA	2022-01-31	2000-10-27	1120	LA MATANZA Nº 03	Buenos Aires	Nacional	2000.0	NaN	SEDAN	08	CHEVROLET	84	CORSA 3P CITI 1.0 N	1	Privado	Física	LOMAS DEL MIRADOR	BUENOS AIRES	Femenino	1991	ARGENTINA	100	6	ARG
2	DENUNCIA DE ROBO O HURTO / RETENCION INDEBIDA	2022-01-11	1994-04-28	2086	CAPITAL FEDERAL Nº 086	Ciudad Autónoma de Bs.As.	Nacional	1994.0	NaN	FUR.VIDRIADO C/ASIEN	37	RENAULT	55	RENAULT TRAFIC	1	Privado	Física	C.AUTONOMA DE BS.AS	C.AUTONOMA DE BS.AS	Masculino	1971	PERU	100	2	NaN
3	DENUNCIA DE ROBO O HURTO / RETENCION INDEBIDA	2022-01-10	1992-03-27	1119	MORON Nº 03	Buenos Aires	Importado	1991.0	NaN	RURAL 4 PTAS.	NaN	NISSAN	NaN	PATHFINDER SE-V6	1	Privado	Física	HAEDO	BUENOS AIRES	No identificado	1982	ARGENTINA	50	6	ARG
4	DENUNCIA DE ROBO O HURTO / RETENCION INDEBIDA	2022-01-24	2000-11-01	1118	QUILMES Nº 2	Buenos Aires	Nacional	2000.0	NaN	SEDAN 4 PUERTAS	08	CHEVROLET	57	CORSA 4 PUERTAS WIND 1.6 MPFI	1	Privado	Física	QUILMES OESTE	BUENOS AIRES	Masculino	1947	ARGENTINA	100	6	ARG
5	DENUNCIA DE ROBO O HURTO / RETENCION INDEBIDA	2022-01-03	1995-11-10	4058	EMBALSE	Córdoba	Nacional	1995.0	NaN	CHASIS C/CABINA P/CA	29	MERCEDES BENZ	55	BM386 VERSION 1633	1	Privado	Física	VILLA RUMIPAL	CORDOBA	Masculino	1980	ARGENTINA	100	14	ARG
6	DENUNCIA DE ROBO O HURTO / RETENCION INDEBIDA	2022-01-12	2000-04-05	1113	SAN VICENTE	Buenos Aires	Nacional	2000.0	NaN	3 PTAS	08	CHEVROLET	77	CORSA 3 DOOR WIND 1.6 MPFI	1	Privado	Física	GUERNICA	BUENOS AIRES	Femenino	1978	PARAGUAY	100	6	PRY
7	DENUNCIA DE ROBO O HURTO / RETENCION INDEBIDA	2022-01-18	1997-11-18	6004	MACHAGAI	Chaco	Protocolo 21	1997.0	NaN	FURGON VIDRIADO C/ ASIENTOS	112	RENAULT	122	EXPRESS	1	Privado	Física	COLONIAS UNIDAS	CHACO	Masculino	1976	ARGENTINA	100	22	ARG
8	DENUNCIA DE ROBO O HURTO / RETENCION INDEBIDA	2022-01-06	2014-06-09	2045	CAPITAL FEDERAL Nº 045	Ciudad Autónoma de Bs.As.	Protocolo 21	2014.0	NaN	TRANSPORTE DE CARGA	092	MERCEDES BENZ	272	710	1	Privado	Jurídica	C.AUTONOMA DE BS.AS	C.AUTONOMA DE BS.AS	No aplica	1994	No aplica	100	2	NaN
9	DENUNCIA DE ROBO O HURTO / RETENCION INDEBIDA	2022-01-28	1998-06-12	2078	CAPITAL FEDERAL Nº 078	Ciudad Autónoma de Bs.As.	Nacional	1998.0	NaN	BERLINA 5 PTAS.	37	RENAULT	CD	CLIO DIESEL 5P DA AA	1	Privado	Física	C.AUTONOMA DE BS.AS	C.AUTONOMA DE BS.AS	No identificado	1978	ARGENTINA	100	2	ARG

In [15]:

df2.shape

Out[15]:

(2676, 25)

In [16]:

df2.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 2676 entries, 0 to 2675
Data columns (total 25 columns):
 #   Column                          Non-Null Count  Dtype  
---  ------                          --------------  -----  
 0   tramite_tipo                    2676 non-null   object 
 1   tramite_fecha                   2676 non-null   object 
 2   fecha_inscripcion_inicial       2676 non-null   object 
 3   registro_seccional_codigo       2676 non-null   int64  
 4   registro_seccional_descripcion  2676 non-null   object 
 5   registro_seccional_provincia    2676 non-null   object 
 6   automotor_origen                2676 non-null   object 
 7   automotor_anio_modelo           2671 non-null   float64
 8   automotor_tipo_codigo           2601 non-null   object 
 9   automotor_tipo_descripcion      2676 non-null   object 
 10  automotor_marca_codigo          2644 non-null   object 
 11  automotor_marca_descripcion     2676 non-null   object 
 12  automotor_modelo_codigo         2640 non-null   object 
 13  automotor_modelo_descripcion    2676 non-null   object 
 14  automotor_uso_codigo            2676 non-null   int64  
 15  automotor_uso_descripcion       2676 non-null   object 
 16  titular_tipo_persona            2676 non-null   object 
 17  titular_domicilio_localidad     2674 non-null   object 
 18  titular_domicilio_provincia     2676 non-null   object 
 19  titular_genero                  2676 non-null   object 
 20  titular_anio_nacimiento         2676 non-null   int64  
 21  titular_pais_nacimiento         2676 non-null   object 
 22  titular_porcentaje_titularidad  2676 non-null   int64  
 23  titular_domicilio_provincia_id  2676 non-null   int64  
 24  titular_pais_nacimiento_id      2550 non-null   object 
dtypes: float64(1), int64(5), object(19)
memory usage: 522.8+ KB

In [19]:

df2['tramite_fecha']=pd.to_datetime(df2['tramite_fecha']) # Formato datetime python 
monthly_accidents =df2.groupby(df2['tramite_fecha'].dt.to_period('D')).size()
monthly_accidents.plot.line(figsize=(12,6))

Out[19]:

<matplotlib.axes._subplots.AxesSubplot at 0x7f49a147fa90>

Marca mas robada¶

In [25]:

df2['automotor_marca_descripcion'].value_counts().plot(kind='bar', figsize=(12,6), ylabel='Frecuencia', title='Marcas mas robadas')

Out[25]:

<matplotlib.axes._subplots.AxesSubplot at 0x7f49a0f4b050>

In [30]:

df2['automotor_modelo_descripcion'].value_counts().iloc[0:50].plot(kind='bar', figsize=(12,6), ylabel='Frecuencia', title='Modelos mas robadas')

Out[30]:

<matplotlib.axes._subplots.AxesSubplot at 0x7f499cd140d0>

In [31]:

df2['titular_genero'].value_counts().plot(kind='bar', figsize=(12,6), ylabel='Frecuencia', title='Robos por genero')

Out[31]:

<matplotlib.axes._subplots.AxesSubplot at 0x7f499cb31210>

In [33]:

df2['titular_pais_nacimiento'].value_counts().plot(kind='bar', figsize=(12,6), ylabel='Frecuencia', title='Procedencia del titular')

Out[33]:

<matplotlib.axes._subplots.AxesSubplot at 0x7f499bb1d390>

In [34]:

df2['titular_domicilio_provincia'].value_counts().plot(kind='bar', figsize=(12,6), ylabel='Frecuencia', title='Procedencia del titular')

Out[34]:

<matplotlib.axes._subplots.AxesSubplot at 0x7f499ba66310>

Actividad colaborativa¶

SQL¶

In [40]:

import sqlite3 as sql
import pandas as pd
import matplotlib.pyplot as plt
conn= sql.connect('nba_salary.sqlite')
conn

Out[40]:

<sqlite3.Connection at 0x7f499ce8de30>

In [42]:

# Visualizar las tablas
cursor = conn.cursor()
cursor.execute("SELECT name FROM sqlite_master WHERE type='table';")
print(cursor. fetchall())

[('NBA_season1718_salary',), ('Seasons_Stats',)]

In [44]:

df_salary = pd.read_sql_query("SELECT * from NBA_season1718_salary", conn)
df_salary.head()

Out[44]:

	X1	Player	Tm	season17_18
0	1.0	Stephen Curry	GSW	34682550.0
1	2.0	LeBron James	CLE	33285709.0
2	3.0	Paul Millsap	DEN	31269231.0
3	4.0	Gordon Hayward	BOS	29727900.0
4	5.0	Blake Griffin	DET	29512900.0

In [46]:

df_stats = pd.read_sql_query("SELECT * from Seasons_Stats", conn)
df_stats.head()

Out[46]:

	X1	Year	Player	Pos	Age	Tm	G	GS	MP	PER	TS%	3PAr	FTr	ORB%	DRB%	TRB%	AST%	STL%	BLK%	TOV%	USG%	blanl	OWS	DWS	WS	WS/48	blank2	OBPM	DBPM	BPM	VORP	FG	FGA	FG%	3P	3PA	3P%	2P	2PA	2P%	eFG%	FT	FTA	FT%	ORB	DRB	TRB	AST	STL	BLK	TOV	PF	PTS
0	0.0	1950.0	Curly Armstrong	G-F	31.0	FTW	63.0	NaN	NaN	NaN	0.368	NaN	0.467	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	None	-0.1	3.6	3.5	NaN	None	NaN	NaN	NaN	NaN	144.0	516.0	0.279	NaN	NaN	NaN	144.0	516.0	0.279	0.279	170.0	241.0	0.705	NaN	NaN	NaN	176.0	NaN	NaN	NaN	217.0	458.0
1	1.0	1950.0	Cliff Barker	SG	29.0	INO	49.0	NaN	NaN	NaN	0.435	NaN	0.387	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	None	1.6	0.6	2.2	NaN	None	NaN	NaN	NaN	NaN	102.0	274.0	0.372	NaN	NaN	NaN	102.0	274.0	0.372	0.372	75.0	106.0	0.708	NaN	NaN	NaN	109.0	NaN	NaN	NaN	99.0	279.0
2	2.0	1950.0	Leo Barnhorst	SF	25.0	CHS	67.0	NaN	NaN	NaN	0.394	NaN	0.259	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	None	0.9	2.8	3.6	NaN	None	NaN	NaN	NaN	NaN	174.0	499.0	0.349	NaN	NaN	NaN	174.0	499.0	0.349	0.349	90.0	129.0	0.698	NaN	NaN	NaN	140.0	NaN	NaN	NaN	192.0	438.0
3	3.0	1950.0	Ed Bartels	F	24.0	TOT	15.0	NaN	NaN	NaN	0.312	NaN	0.395	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	None	-0.5	-0.1	-0.6	NaN	None	NaN	NaN	NaN	NaN	22.0	86.0	0.256	NaN	NaN	NaN	22.0	86.0	0.256	0.256	19.0	34.0	0.559	NaN	NaN	NaN	20.0	NaN	NaN	NaN	29.0	63.0
4	4.0	1950.0	Ed Bartels	F	24.0	DNN	13.0	NaN	NaN	NaN	0.308	NaN	0.378	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	None	-0.5	-0.1	-0.6	NaN	None	NaN	NaN	NaN	NaN	21.0	82.0	0.256	NaN	NaN	NaN	21.0	82.0	0.256	0.256	17.0	31.0	0.548	NaN	NaN	NaN	20.0	NaN	NaN	NaN	27.0	59.0

JSON¶

In [48]:

# De una URL
URL = 'http://raw.githubusercontent.com/BindiChen/machine-learning/master/data-analysis/027-pandas-convert-json/data/simple.json'
df = pd.read_json(URL)
df.head(10)

Out[48]:

	id	name	math	physics	chemistry
0	A001	Tom	60	66	61
1	A002	James	89	76	51
2	A003	Jenny	79	90	78

In [53]:

# Miremos este caso especial
df = pd.read_json('nested_json.json')
df # tenemos un JSON nested

Out[53]:

	school_name	class	students
0	ABC primary school	Year 1	{'id': 'A001', 'name': 'Tom', 'math': 60, 'phy...
1	ABC primary school	Year 1	{'id': 'A002', 'name': 'James', 'math': 89, 'p...
2	ABC primary school	Year 1	{'id': 'A003', 'name': 'Jenny', 'math': 79, 'p...

In [55]:

import json
# Normalizar los datos
with open('nested_json.json','r') as f:
    data = json.loads(f.read())# Flatten data
df_nested_list = pd.json_normalize(data, record_path =['students'])
df_nested_list

Out[55]:

	id	name	math	physics	chemistry
0	A001	Tom	60	66	61
1	A002	James	89	76	51
2	A003	Jenny	79	90	78

In [58]:

# Combinando
df_nested_list = pd.json_normalize(
    data, 
    record_path =['students'], 
    meta=['school_name', 'class']
)
df_nested_list

Out[58]:

	id	name	math	physics	chemistry	school_name	class
0	A001	Tom	60	66	61	ABC primary school	Year 1
1	A002	James	89	76	51	ABC primary school	Year 1
2	A003	Jenny	79	90	78	ABC primary school	Year 1

APIs¶

In [59]:

!pip install pytrends

Collecting pytrends
  Downloading pytrends-4.8.0.tar.gz (19 kB)
Requirement already satisfied: requests>=2.0 in /usr/local/lib/python3.7/dist-packages (from pytrends) (2.23.0)
Requirement already satisfied: pandas>=0.25 in /usr/local/lib/python3.7/dist-packages (from pytrends) (1.3.5)
Requirement already satisfied: lxml in /usr/local/lib/python3.7/dist-packages (from pytrends) (4.2.6)
Requirement already satisfied: python-dateutil>=2.7.3 in /usr/local/lib/python3.7/dist-packages (from pandas>=0.25->pytrends) (2.8.2)
Requirement already satisfied: numpy>=1.17.3 in /usr/local/lib/python3.7/dist-packages (from pandas>=0.25->pytrends) (1.21.5)
Requirement already satisfied: pytz>=2017.3 in /usr/local/lib/python3.7/dist-packages (from pandas>=0.25->pytrends) (2018.9)
Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.7/dist-packages (from python-dateutil>=2.7.3->pandas>=0.25->pytrends) (1.15.0)
Requirement already satisfied: chardet<4,>=3.0.2 in /usr/local/lib/python3.7/dist-packages (from requests>=2.0->pytrends) (3.0.4)
Requirement already satisfied: idna<3,>=2.5 in /usr/local/lib/python3.7/dist-packages (from requests>=2.0->pytrends) (2.10)
Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.7/dist-packages (from requests>=2.0->pytrends) (2021.10.8)
Requirement already satisfied: urllib3!=1.25.0,!=1.25.1,<1.26,>=1.21.1 in /usr/local/lib/python3.7/dist-packages (from requests>=2.0->pytrends) (1.24.3)
Building wheels for collected packages: pytrends
  Building wheel for pytrends (setup.py) ... done
  Created wheel for pytrends: filename=pytrends-4.8.0-py3-none-any.whl size=16126 sha256=7ecca0a570dbdae27d5ffb7491b366d6d83b83b36c59edde3ddb4e625078c3d0
  Stored in directory: /root/.cache/pip/wheels/07/6f/5c/8174f98dec1bfbc7d5da4092854afcbcff4b26c3d9b66b5183
Successfully built pytrends
Installing collected packages: pytrends
Successfully installed pytrends-4.8.0

In [64]:

import pandas as pd
from pytrends.request import TrendReq
pytrends = TrendReq()
keywords = ["barcelona", "Copperfield", "Madrid"]
pytrends.build_payload(keywords, cat=0, geo='', gprop='') # Datos de los ultimos 5 años
stop_queries = pytrends.interest_over_time()[keywords]
stop_queries.head()

Out[64]:

	barcelona	Copperfield	Madrid
date
2017-02-26	33	0	49
2017-03-05	59	0	44
2017-03-12	31	0	43
2017-03-19	29	0	31
2017-03-26	22	0	29

In [66]:

stop_queries.shape

Out[66]:

(261, 3)

In [65]:

pytrends.build_payload?

yfinance¶

In [70]:

!pip install yfinance

Collecting yfinance
  Downloading yfinance-0.1.70-py2.py3-none-any.whl (26 kB)
Requirement already satisfied: multitasking>=0.0.7 in /usr/local/lib/python3.7/dist-packages (from yfinance) (0.0.10)
Collecting lxml>=4.5.1
  Downloading lxml-4.8.0-cp37-cp37m-manylinux_2_17_x86_64.manylinux2014_x86_64.manylinux_2_24_x86_64.whl (6.4 MB)
     |████████████████████████████████| 6.4 MB 6.5 MB/s 
Requirement already satisfied: pandas>=0.24.0 in /usr/local/lib/python3.7/dist-packages (from yfinance) (1.3.5)
Requirement already satisfied: numpy>=1.15 in /usr/local/lib/python3.7/dist-packages (from yfinance) (1.21.5)
Collecting requests>=2.26
  Downloading requests-2.27.1-py2.py3-none-any.whl (63 kB)
     |████████████████████████████████| 63 kB 1.7 MB/s 
Requirement already satisfied: python-dateutil>=2.7.3 in /usr/local/lib/python3.7/dist-packages (from pandas>=0.24.0->yfinance) (2.8.2)
Requirement already satisfied: pytz>=2017.3 in /usr/local/lib/python3.7/dist-packages (from pandas>=0.24.0->yfinance) (2018.9)
Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.7/dist-packages (from python-dateutil>=2.7.3->pandas>=0.24.0->yfinance) (1.15.0)
Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.7/dist-packages (from requests>=2.26->yfinance) (2021.10.8)
Requirement already satisfied: idna<4,>=2.5 in /usr/local/lib/python3.7/dist-packages (from requests>=2.26->yfinance) (2.10)
Requirement already satisfied: urllib3<1.27,>=1.21.1 in /usr/local/lib/python3.7/dist-packages (from requests>=2.26->yfinance) (1.24.3)
Requirement already satisfied: charset-normalizer~=2.0.0 in /usr/local/lib/python3.7/dist-packages (from requests>=2.26->yfinance) (2.0.12)
Installing collected packages: requests, lxml, yfinance
  Attempting uninstall: requests
    Found existing installation: requests 2.23.0
    Uninstalling requests-2.23.0:
      Successfully uninstalled requests-2.23.0
  Attempting uninstall: lxml
    Found existing installation: lxml 4.2.6
    Uninstalling lxml-4.2.6:
      Successfully uninstalled lxml-4.2.6
ERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.
google-colab 1.0.0 requires requests~=2.23.0, but you have requests 2.27.1 which is incompatible.
datascience 0.10.6 requires folium==0.2.1, but you have folium 0.8.3 which is incompatible.
Successfully installed lxml-4.8.0 requests-2.27.1 yfinance-0.1.70

In [72]:

import yfinance as yf
pfe = yf.Ticker('PFE')
pfe.info

Out[72]:

{'52WeekChange': 0.41644406,
 'SandP52WeekChange': 0.123744845,
 'address1': '235 East 42nd Street',
 'algorithm': None,
 'annualHoldingsTurnover': None,
 'annualReportExpenseRatio': None,
 'ask': 47.63,
 'askSize': 1800,
 'averageDailyVolume10Day': 31897490,
 'averageVolume': 41594635,
 'averageVolume10days': 31897490,
 'beta': 0.684733,
 'beta3Year': None,
 'bid': 47.65,
 'bidSize': 1200,
 'bookValue': 13.49,
 'category': None,
 'circulatingSupply': None,
 'city': 'New York',
 'companyOfficers': [],
 'country': 'United States',
 'currency': 'USD',
 'currentPrice': 47.72,
 'currentRatio': None,
 'dateShortInterest': 1643587200,
 'dayHigh': 48.03,
 'dayLow': 45.74,
 'debtToEquity': 52.496,
 'dividendRate': 1.6,
 'dividendYield': 0.0335,
 'earningsGrowth': 4.575,
 'earningsQuarterlyGrowth': 4.712,
 'ebitda': 31947665408,
 'ebitdaMargins': 0.39301997,
 'enterpriseToEbitda': 8.711,
 'enterpriseToRevenue': 3.424,
 'enterpriseValue': 278298001408,
 'exDividendDate': 1643241600,
 'exchange': 'NYQ',
 'exchangeTimezoneName': 'America/New_York',
 'exchangeTimezoneShortName': 'EST',
 'expireDate': None,
 'fiftyDayAverage': 54.1052,
 'fiftyTwoWeekHigh': 61.71,
 'fiftyTwoWeekLow': 33.44,
 'financialCurrency': 'USD',
 'fiveYearAverageReturn': None,
 'fiveYearAvgDividendYield': 3.66,
 'floatShares': 5607197603,
 'forwardEps': 52.23,
 'forwardPE': 0.91365117,
 'freeCashflow': None,
 'fromCurrency': None,
 'fundFamily': None,
 'fundInceptionDate': None,
 'gmtOffSetMilliseconds': '-18000000',
 'grossMargins': 0.62281,
 'grossProfits': 50627000000,
 'heldPercentInsiders': 0.0004,
 'heldPercentInstitutions': 0.68464,
 'impliedSharesOutstanding': 0,
 'industry': 'Drug Manufacturers—General',
 'isEsgPopulated': False,
 'lastCapGain': None,
 'lastDividendDate': 1643241600,
 'lastDividendValue': 0.4,
 'lastFiscalYearEnd': 1640908800,
 'lastMarket': None,
 'lastSplitDate': 1605571200,
 'lastSplitFactor': '1054:1000',
 'legalType': None,
 'logo_url': 'https://logo.clearbit.com/pfizer.com',
 'longBusinessSummary': 'Pfizer Inc. discovers, develops, manufactures, markets, distributes, and sells biopharmaceutical products worldwide. It offers medicines and vaccines in various therapeutic areas, including cardiovascular metabolic and pain under the Eliquis, Chantix/Champix, and Premarin family brands; biologics, small molecules, immunotherapies, and biosimilars under the Ibrance, Xtandi, Sutent, Inlyta, Retacrit, Lorbrena, and Braftovi brands; and sterile injectable and anti-infective medicines under the Sulperazon, Medrol, Zithromax, Vfend, and Panzyga brands. The company also provides medicines and vaccines in various therapeutic areas, such as pneumococcal disease, meningococcal disease, tick-borne encephalitis, and COVID-19 under the Prevnar 13/Prevenar 13 (pediatric/adult), Nimenrix, FSME/IMMUN-TicoVac, Trumenba, and the Pfizer-BioNTech COVID-19 vaccine brands; biosimilars for chronic immune and inflammatory diseases under the Xeljanz, Enbrel, Inflectra, and Eucrisa/Staquis brands; and amyloidosis, hemophilia, and endocrine diseases under the Vyndaqel/Vyndamax, BeneFIX, and Genotropin brands. In addition, the company is involved in the contract manufacturing business. It serves wholesalers, retailers, hospitals, clinics, government agencies, pharmacies, and individual provider offices, as well as disease control and prevention centers. The company has collaboration agreements with Bristol-Myers Squibb Company; Astellas Pharma US, Inc.; Myovant Sciences Ltd.; Akcea Therapeutics, Inc; Merck KGaA; Valneva SE; BioNTech SE; Arvinas, Inc.; and Syapse, Inc. Pfizer Inc. was founded in 1849 and is headquartered in New York, New York.',
 'longName': 'Pfizer Inc.',
 'market': 'us_market',
 'marketCap': 268346261504,
 'maxAge': 1,
 'maxSupply': None,
 'messageBoardId': 'finmb_162270',
 'morningStarOverallRating': None,
 'morningStarRiskRating': None,
 'mostRecentQuarter': 1640908800,
 'navPrice': None,
 'netIncomeToCommon': 22413000704,
 'nextFiscalYearEnd': 1703980800,
 'numberOfAnalystOpinions': 23,
 'open': 45.81,
 'openInterest': None,
 'operatingCashflow': None,
 'operatingMargins': 0.33025002,
 'payoutRatio': 0.3969,
 'pegRatio': 0.94,
 'phone': '212 733 2323',
 'preMarketPrice': None,
 'previousClose': 45.96,
 'priceHint': 2,
 'priceToBook': 3.5374353,
 'priceToSalesTrailing12Months': 3.3011792,
 'profitMargins': 0.27038,
 'quickRatio': None,
 'quoteType': 'EQUITY',
 'recommendationKey': 'buy',
 'recommendationMean': 2.4,
 'regularMarketDayHigh': 48.03,
 'regularMarketDayLow': 45.74,
 'regularMarketOpen': 45.81,
 'regularMarketPreviousClose': 45.96,
 'regularMarketPrice': 47.72,
 'regularMarketVolume': 29894270,
 'returnOnAssets': None,
 'returnOnEquity': 0.32212,
 'revenueGrowth': 1.04,
 'revenuePerShare': 14.513,
 'revenueQuarterlyGrowth': None,
 'sector': 'Healthcare',
 'sharesOutstanding': 5612870144,
 'sharesPercentSharesOut': 0.0123000005,
 'sharesShort': 69221304,
 'sharesShortPreviousMonthDate': 1640908800,
 'sharesShortPriorMonth': 58635375,
 'shortName': 'Pfizer, Inc.',
 'shortPercentOfFloat': 0.0123000005,
 'shortRatio': 1.8,
 'startDate': None,
 'state': 'NY',
 'strikePrice': None,
 'symbol': 'PFE',
 'targetHighPrice': 76,
 'targetLowPrice': 46,
 'targetMeanPrice': 59.86,
 'targetMedianPrice': 59.5,
 'threeYearAverageReturn': None,
 'toCurrency': None,
 'totalAssets': None,
 'totalCash': 29702000640,
 'totalCashPerShare': 5.292,
 'totalDebt': 39879000064,
 'totalRevenue': 81288003584,
 'tradeable': False,
 'trailingAnnualDividendRate': 1.56,
 'trailingAnnualDividendYield': 0.033942558,
 'trailingEps': 3.854,
 'trailingPE': 12.381941,
 'trailingPegRatio': 1.0195,
 'twoHundredDayAverage': 46.707,
 'volume': 29894270,
 'volume24Hr': None,
 'volumeAllCurrencies': None,
 'website': 'https://www.pfizer.com',
 'yield': None,
 'ytdReturn': None,
 'zip': '10017'}

In [73]:

# Scar los dividendos
pfe.actions

Out[73]:

	Dividends	Stock Splits
Date
1972-08-29	0.003163	0.0
1972-11-28	0.004151	0.0
1973-02-28	0.003163	0.0
1973-05-30	0.003163	0.0
1973-08-28	0.003163	0.0
...	...	...
2021-01-28	0.390000	0.0
2021-05-06	0.390000	0.0
2021-07-29	0.390000	0.0
2021-11-04	0.390000	0.0
2022-01-27	0.400000	0.0

205 rows × 2 columns

In [75]:

pfe.history?

In [74]:

# period: Valid periods: 1d,5d,1mo,3mo,6mo,1y,2y,5y,10y,ytd,max
# Tambien se puede colocar start, end 
hist = pfe.history(period="6mo")
hist

Out[74]:

	Open	High	Low	Close	Volume	Dividends	Stock Splits
Date
2021-08-26	46.928141	47.380697	46.534612	46.613319	29029300	0.0	0
2021-08-27	46.603480	46.731378	45.777073	45.845940	32874400	0.0	0
2021-08-30	45.452413	46.396878	45.413060	46.003349	25774400	0.0	0
2021-08-31	45.964000	46.377202	45.186782	45.324516	34547800	0.0	0
2021-09-01	45.442572	45.511439	44.685032	45.295002	32900100	0.0	0
...	...	...	...	...	...	...	...
2022-02-18	48.779999	49.520000	48.200001	48.529999	26939500	0.0	0
2022-02-22	47.540001	48.310001	47.349998	47.529999	34814800	0.0	0
2022-02-23	47.590000	48.160000	46.779999	46.869999	25793000	0.0	0
2022-02-24	45.860001	46.759998	45.400002	45.959999	44227300	0.0	0
2022-02-25	45.810001	48.029999	45.740002	47.720001	31191800	0.0	0

127 rows × 7 columns

Lectura en serie de datos¶

In [76]:

tickers = ['TSLA', 'API', 'LMND','MRK','GOOG']
import matplotlib.pyplot as plt
for i,ticker in enumerate(tickers):
  current_ticker = yf.Ticker(ticker)
  plt.subplot(len(tickers),1,i+1)
  current_ticker.history(period='365d')['Close'].plot(figsize=   (16,60), title='Precio historico de 1 de un año para: '+ticker)

In [77]:

# Informacion del primero
ticker = tickers[0]
yf_info = yf.Ticker(ticker).info
print(ticker)

TSLA

In [78]:

# Creciemito de 52 semanas
yf_info['52WeekChange']

Out[78]:

0.1272775

In [79]:

# Sacando algunas estadisticas de interes
stock_52w_change = []
profitsMargins = []
tickers = ['NVS','JNJ','ABBV','AMGN']
for ticker in tickers:
  print(ticker)
  current_ticker = yf.Ticker(ticker)
  current_ticker_info = current_ticker.info
  stock_52w_change.append(current_ticker_info['52WeekChange'])
  profitsMargins.append(current_ticker_info['profitMargins'])

NVS
JNJ
ABBV
AMGN

In [80]:

stock_52w_change

Out[80]:

[0.0020582676, 0.041928172, 0.3793931, -4.4047832e-05]

In [81]:

profitsMargins

Out[81]:

[0.45428002, 0.22264, 0.20538001, 0.22684]

In [83]:

import pandas as pd
df = pd.DataFrame([stock_52w_change, profitsMargins], columns=tickers, index={'52w change', 'profitMargins'})
print(df)

                    NVS       JNJ      ABBV      AMGN
profitMargins  0.002058  0.041928  0.379393 -0.000044
52w change     0.454280  0.222640  0.205380  0.226840

In [84]:

import matplotlib.ticker as mtick
ax = df.plot.bar()
ax.yaxis.set_major_formatter(mtick.PercentFormatter(xmax=1))
ax.set_title('Comparando margenes de profit y tasas de crecimiento en 52 semanas para stocks farmacias')

Out[84]:

Text(0.5, 1.0, 'Comparando margenes de profit y tasas de crecimiento en 52 semanas para stocks farmacias')

Created in Deepnote