Manipulacion de series y dataframes (Ejemplo en vivo)¶
In [ ]:
# Vamos a crear una estructura de serie
import pandas as pd
serie= pd.Series(data=[1,2,3,4],index=['David','Juan',1,2],name='SerieX')
serie
Out[ ]:
David 1 Juan 2 1 3 2 4 Name: SerieX, dtype: int64
In [ ]:
# Algunas propiedades de la serie
print('Indice de la serie:',serie.index)
print('Valores de la serie:',serie.values)
print('Longitud de serie:',serie.count())
# Verificar nulos en la serie
serie.isna()
Indice de la serie: Index(['David', 'Juan', 1, 2], dtype='object') Valores de la serie: [1 2 3 4] Longitud de serie: 4
Out[ ]:
David False Juan False 1 False 2 False Name: SerieX, dtype: bool
In [ ]:
# Filtrar un indice
serie.loc['David']
Out[ ]:
1
In [ ]:
# Filtrar pero de otra forma
serie.iloc[0]
Out[ ]:
1
In [ ]:
# Filtrar varios indices
serie.loc[['David',1]]
Out[ ]:
David 1 1 3 Name: SerieX, dtype: int64
In [ ]:
import numpy as np
serie_2= pd.Series(data=[1,2,5,6,np.nan , 20,np.nan])
serie_2
Out[ ]:
0 1.0 1 2.0 2 5.0 3 6.0 4 NaN 5 20.0 6 NaN dtype: float64
In [ ]:
# Suma de nulos
serie_2.isnull().sum()
Out[ ]:
2
In [ ]:
# Conteo de valores no tiene en cuenta los NaN
serie_2.value_counts()
Out[ ]:
20.0 1 6.0 1 5.0 1 2.0 1 1.0 1 dtype: int64
In [ ]:
# Creemos un dataframe
valores= [1000, 3500, 3000, 4000, 3000 , 2450]
nombres= ['David','Juan','Pedro','Lucas','Andrea','Felipe']
genero=['M','M','M','M','F','M']
df= pd.DataFrame(data={'salarios':valores, 'genero':genero},index=nombres)
df
Out[ ]:
| salarios | genero | |
|---|---|---|
| David | 1000 | M |
| Juan | 3500 | M |
| Pedro | 3000 | M |
| Lucas | 4000 | M |
| Andrea | 3000 | F |
| Felipe | 2450 | M |
In [ ]:
# filtremos por genero
df_m= df[df['genero']=='M']
print(df_m.head())
print('------')
df_f=df[df['genero']=='F']
print(df_f.head())
salarios genero
David 1000 M
Juan 3500 M
Pedro 3000 M
Lucas 4000 M
Felipe 2450 M
------
salarios genero
Andrea 3000 F
In [ ]:
# Otra forma
condicion_h = df['genero']=='M'
condicion_m = df['genero']=='F'
print(df[condicion_h])
print('-------------')
print(df[condicion_m])
salarios genero
David 1000 M
Juan 3500 M
Pedro 3000 M
Lucas 4000 M
Felipe 2450 M
-------------
salarios genero
Andrea 3000 F
In [ ]:
# Algunas cosas adicionales
df.loc['David'][['salarios','genero']]
Out[ ]:
salarios 1000 genero M Name: David, dtype: object
In [ ]:
df.loc[['David','Juan']][['salarios','genero']]
Out[ ]:
| salarios | genero | |
|---|---|---|
| David | 1000 | M |
| Juan | 3500 | M |
Lectura de csv t txt¶
In [ ]:
from google.colab import drive
import os
drive.mount('/content/gdrive')
Drive already mounted at /content/gdrive; to attempt to forcibly remount, call drive.mount("/content/gdrive", force_remount=True).
In [ ]:
%cd '/content/gdrive/MyDrive'
df= pd.read_csv('winequality-red.csv',sep=',')
print(df[['density','pH','sulphates','alcohol','quality']].head())
/content/gdrive/MyDrive density pH sulphates alcohol quality 0 0.9978 3.51 0.56 9.4 5 1 0.9968 3.20 0.68 9.8 5 2 0.9970 3.26 0.65 9.8 5 3 0.9980 3.16 0.58 9.8 6 4 0.9978 3.51 0.56 9.4 5
In [ ]:
%cd '/content/gdrive/MyDrive'
df= pd.read_csv('pokemon_data.txt',delimiter='\t')
print(df[['Name','Type 1','HP','Attack','Defense']].head())
/content/gdrive/MyDrive
Name Type 1 HP Attack Defense
0 Bulbasaur Grass 45 49 49
1 Ivysaur Grass 60 62 63
2 Venusaur Grass 80 82 83
3 VenusaurMega Venusaur Grass 80 100 123
4 Charmander Fire 39 52 43
Lectura de xlsx¶
In [ ]:
%cd '/content/gdrive/MyDrive'
df= pd.read_excel('defaultoutput.xlsx')
print(df[['index','ID','Year_Birth','Education','Income']].head())
/content/gdrive/MyDrive index ID Year_Birth Education Income 0 0 5524 1957 Graduation 58138.0 1 1 2174 1954 Graduation 46344.0 2 2 4141 1965 Graduation 71613.0 3 3 6182 1984 Graduation 26646.0 4 4 5324 1981 PhD 58293.0
Lectura desde Github /Apis¶
In [ ]:
import pandas as pd
url = 'https://raw.githubusercontent.com/JJTorresDS/stocks-ds-edu/main/stocks.csv'
df = pd.read_csv(url, index_col=0)
print(df[['AMZN','MCD','SBUX','GOOG','MSFT']].head(5).round(1))
AMZN MCD SBUX GOOG MSFT formatted_date 2016-01-01 587.0 106.3 54.4 743.0 49.9 2016-02-01 552.5 100.7 52.1 697.8 46.0 2016-03-01 593.6 108.8 53.6 745.0 50.3 2016-04-01 659.6 109.5 50.5 693.0 45.5 2016-05-01 722.8 105.6 49.3 735.7 48.3
In [ ]:
!wget -O cars_clus.csv https://s3-api.us-geo.objectstorage.softlayer.net/cf-courses-data/CognitiveClass/ML0101ENv3/labs/cars_clus.csv
--2021-12-23 17:22:23-- https://s3-api.us-geo.objectstorage.softlayer.net/cf-courses-data/CognitiveClass/ML0101ENv3/labs/cars_clus.csv Resolving s3-api.us-geo.objectstorage.softlayer.net (s3-api.us-geo.objectstorage.softlayer.net)... 67.228.254.196 Connecting to s3-api.us-geo.objectstorage.softlayer.net (s3-api.us-geo.objectstorage.softlayer.net)|67.228.254.196|:443... connected. HTTP request sent, awaiting response... 200 OK Length: 17774 (17K) [text/csv] Saving to: ‘cars_clus.csv’ cars_clus.csv 100%[===================>] 17.36K --.-KB/s in 0s 2021-12-23 17:22:23 (136 MB/s) - ‘cars_clus.csv’ saved [17774/17774]
In [ ]:
filename = 'cars_clus.csv'
#Lectura
pdf = pd.read_csv(filename)
print ("Shape: ", pdf.shape)
print(pdf[['manufact','model','sales','resale']].head(5))
Shape: (159, 16) manufact model sales resale 0 Acura Integra 16.919 16.360 1 Acura TL 39.384 19.875 2 Acura CL 14.114 18.225 3 Acura RL 8.588 29.725 4 Audi A4 20.397 22.255
Adicional (No es necesario en clase)¶
Los datos se encuentran en la siguiente link: Data Acciones
In [ ]:
import pandas as pd
url = 'https://raw.githubusercontent.com/JJTorresDS/stocks-ds-edu/main/stocks.csv'
df = pd.read_csv(url, index_col=0)
print(df.head(5))
MCD SBUX ... MA PYPL formatted_date ... 2021-01-01 203.187836 95.235695 ... 314.746094 234.309998 2021-02-01 201.525894 106.273232 ... 352.569061 259.850006 2021-03-01 220.471207 107.950699 ... 354.761108 242.839996 2021-04-01 232.215775 113.107674 ... 380.676971 262.290009 2021-05-01 230.061630 112.505043 ... 359.701202 260.019989 [5 rows x 14 columns]
In [ ]:
df.head()
Out[ ]:
| MCD | SBUX | GOOG | AMZN | MSFT | JPM | BAC | C | MAR | HLT | RCL | V | MA | PYPL | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| formatted_date | ||||||||||||||
| 2021-01-01 | 203.187836 | 95.235695 | 1835.739990 | 3206.199951 | 230.031799 | 125.517960 | 29.089981 | 56.271671 | 116.309998 | 101.389999 | 65.000000 | 192.070175 | 314.746094 | 234.309998 |
| 2021-02-01 | 201.525894 | 106.273232 | 2036.859985 | 3092.929932 | 230.448318 | 144.598679 | 34.054409 | 64.472809 | 148.070007 | 123.680000 | 93.269997 | 211.093323 | 352.569061 | 259.850006 |
| 2021-03-01 | 220.471207 | 107.950699 | 2068.629883 | 3094.080078 | 234.348633 | 149.570267 | 37.959236 | 71.196068 | 148.110001 | 120.919998 | 85.610001 | 210.764023 | 354.761108 | 242.839996 |
| 2021-04-01 | 232.215775 | 113.107674 | 2410.120117 | 3467.419922 | 250.659698 | 151.122665 | 39.961990 | 69.718323 | 148.520004 | 128.699997 | 86.949997 | 232.494446 | 380.676971 | 262.290009 |
| 2021-05-01 | 230.061630 | 112.505043 | 2411.560059 | 3223.070068 | 248.174774 | 162.320847 | 41.795921 | 77.564484 | 143.580002 | 125.269997 | 93.269997 | 226.263016 | 359.701202 | 260.019989 |
Miremos el tamaño de nuestro datraframe
In [ ]:
df.shape
Out[ ]:
(11, 14)
11 filas x 14 columnas
Las columnas son los precios de acciones de:
- MCD: Macdonals
- SBUX: Starbucks
- GOOG: Google
- AMZN: Amazon
- MSFT: Microsoft
- JPM: JPMorgan Chase & Co.
- BAC: Bank of America Corp
- C: Citigroup
- MAR: Pharma MAr
- HLT: Hoteles Hilton
- RCL: Royal Caribbean Cruises
- V: Visa Inc.
- MA: Mastercard
- PYPL: Paypal
Analizando el comportamiento de una serie de tiempo¶
In [ ]:
df['MCD'].plot(kind='line',figsize=(10,6),xlabel='Fecha', ylabel='Precio Accion', title='Precio Accion vs Fecha')
Out[ ]:
<matplotlib.axes._subplots.AxesSubplot at 0x7f582001d890>
Se observa un comportamiento creciente entre el 01/01/21 hasta el 11/01/2021
Grafiquemos todas las series de tiempo¶
In [ ]:
df.plot(kind='line',figsize=(15,6),xlabel='Fecha', ylabel='Precio Accion', title='Precio Accion vs Fecha').legend(loc='best')
Out[ ]:
<matplotlib.legend.Legend at 0x7f581f89ce90>
En cual accion invertirian y porque?
Al parecer hay una gran diferencia en el comportamiento de las acciones GOOG (Google) y AMZN (Amazon) respecto a las demás, por otro lado podemos analizar el porcentaje de cambio cada día para entender como estan sucediendo los cambios diarios de manera porcentual
In [ ]:
df_cambios=df.pct_change()*100
df_cambios
Out[ ]:
| MCD | SBUX | GOOG | AMZN | MSFT | JPM | BAC | C | MAR | HLT | RCL | V | MA | PYPL | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| formatted_date | ||||||||||||||
| 2021-01-01 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN |
| 2021-02-01 | -0.817934 | 11.589706 | 10.955800 | -3.532843 | 0.181070 | 15.201585 | 17.065765 | 14.574185 | 27.306345 | 21.984418 | 43.492303 | 9.904269 | 12.016978 | 10.900093 |
| 2021-03-01 | 9.400932 | 1.578448 | 1.559749 | 0.037186 | 1.692490 | 3.438197 | 11.466436 | 10.428053 | 0.027010 | -2.231567 | -8.212712 | -0.155997 | 0.621736 | -6.546088 |
| 2021-04-01 | 5.327030 | 4.777157 | 16.508039 | 12.066263 | 6.960171 | 1.037906 | 5.276066 | -2.075599 | 0.276824 | 6.434005 | 1.565233 | 10.310309 | 7.305159 | 8.009394 |
| 2021-05-01 | -0.927648 | -0.532794 | 0.059746 | -7.047022 | -0.991354 | 7.409994 | 4.589188 | 11.254087 | -3.326153 | -2.665113 | 7.268545 | -2.680249 | -5.510123 | -0.865462 |
| 2021-06-01 | -0.694454 | -1.427060 | 3.929407 | 6.735499 | 8.749413 | -5.297128 | -2.736500 | -10.113065 | -4.917118 | -3.711977 | -8.566525 | 3.017877 | 1.250767 | 12.099078 |
| 2021-07-01 | 5.073810 | 8.603875 | 7.904013 | -3.272226 | 5.171653 | -2.417375 | -6.569289 | -4.424032 | 6.929381 | 8.978606 | -9.861628 | 5.375928 | 5.710923 | -5.472078 |
| 2021-08-01 | -2.163073 | -3.244665 | 7.573531 | 4.303413 | 5.956268 | 5.990058 | 8.837327 | 7.138355 | -7.425672 | -5.013310 | 7.623261 | -7.017328 | -10.183470 | 4.765363 |
| 2021-09-01 | 2.093757 | -5.746744 | -8.384662 | -5.351807 | -6.433113 | 2.338235 | 1.676649 | -2.405785 | 9.582653 | 5.806503 | 7.518425 | -2.639177 | 0.418791 | -9.855890 |
| 2021-10-01 | 1.841486 | -3.843711 | 11.259473 | 2.660238 | 17.629108 | 3.787643 | 13.132735 | -1.453400 | 8.055918 | 8.962226 | -5.081503 | -4.929288 | -3.497477 | -10.614502 |
| 2021-11-01 | -0.386887 | 3.365701 | -3.924242 | 3.992378 | -0.310593 | -5.946732 | -6.927583 | -7.227223 | -7.786531 | -6.168802 | -17.304276 | -8.499787 | -6.021122 | -20.508189 |
In [ ]:
df_cambios.plot(kind='line',figsize=(15,6),xlabel='Fecha', ylabel='Porcentaje de cambio', title='Precio Accion vs Fecha').legend(loc='best')
Out[ ]:
<matplotlib.legend.Legend at 0x7f581f8318d0>
Siguen pensando lo mismo respecto a la pregunta anterior Teniendo en cuenta esta información cual sería a la acción en la cual invertirian?
La respuesta en que accion invertir no solo pasa por el valor de la acción en si sino mas bien por factores asociados a la volatididad que pueda tener la acción.
Cuando se prefiere aversion al riesgo (evitar incertidumbre en inversiones) se requieren acciones que tengan poca volatilidad. 🧱 🈶 ☁ 🪗 ❎
Por el contrario cuando se asumen riesgos las inversiones podrían enfocarse en aquellas acciones con mayor volatilidad ⏰ ⚗ ⛹ ♟
Grafico interactivo¶
Para generar un grafico dinamico podemos usar plotly con el fin de poder establecer el valor en cada momento del tiempo para cada accion
Ademas debemos hacer uso de la funcion melt que explicaremos posteriormente mas adelante pero consiste en formato long del dataframe para facilitar el grafico en plotly
In [ ]:
df_n=df.copy()
df_n['Fecha']=df.index
df_n= df_n.reset_index(drop=True)
df_n
Out[ ]:
| MCD | SBUX | GOOG | AMZN | MSFT | JPM | BAC | C | MAR | HLT | RCL | V | MA | PYPL | Fecha | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 203.187836 | 95.235695 | 1835.739990 | 3206.199951 | 230.031799 | 125.517960 | 29.089981 | 56.271671 | 116.309998 | 101.389999 | 65.000000 | 192.070175 | 314.746094 | 234.309998 | 2021-01-01 |
| 1 | 201.525894 | 106.273232 | 2036.859985 | 3092.929932 | 230.448318 | 144.598679 | 34.054409 | 64.472809 | 148.070007 | 123.680000 | 93.269997 | 211.093323 | 352.569061 | 259.850006 | 2021-02-01 |
| 2 | 220.471207 | 107.950699 | 2068.629883 | 3094.080078 | 234.348633 | 149.570267 | 37.959236 | 71.196068 | 148.110001 | 120.919998 | 85.610001 | 210.764023 | 354.761108 | 242.839996 | 2021-03-01 |
| 3 | 232.215775 | 113.107674 | 2410.120117 | 3467.419922 | 250.659698 | 151.122665 | 39.961990 | 69.718323 | 148.520004 | 128.699997 | 86.949997 | 232.494446 | 380.676971 | 262.290009 | 2021-04-01 |
| 4 | 230.061630 | 112.505043 | 2411.560059 | 3223.070068 | 248.174774 | 162.320847 | 41.795921 | 77.564484 | 143.580002 | 125.269997 | 93.269997 | 226.263016 | 359.701202 | 260.019989 | 2021-05-01 |
| 5 | 228.463959 | 110.899529 | 2506.320068 | 3440.159912 | 269.888611 | 153.722504 | 40.652176 | 69.720337 | 136.520004 | 120.620003 | 85.279999 | 233.091354 | 364.200226 | 291.480011 | 2021-06-01 |
| 6 | 240.055786 | 120.441185 | 2704.419922 | 3327.590088 | 283.846313 | 150.006454 | 37.981617 | 66.635887 | 145.979996 | 131.449997 | 76.870003 | 245.622177 | 384.999420 | 275.529999 | 2021-07-01 |
| 7 | 234.863205 | 116.533272 | 2909.239990 | 3470.790039 | 300.752960 | 158.991928 | 41.338177 | 71.392593 | 135.139999 | 124.860001 | 82.730003 | 228.386063 | 345.793121 | 288.660004 | 2021-08-01 |
| 8 | 239.780670 | 109.836403 | 2665.310059 | 3285.040039 | 281.405182 | 162.709534 | 42.031273 | 69.675041 | 148.089996 | 132.110001 | 88.949997 | 222.358551 | 347.241272 | 260.209991 | 2021-09-01 |
| 9 | 244.196198 | 105.614609 | 2965.409912 | 3372.429932 | 331.014404 | 168.872391 | 47.551128 | 68.662384 | 160.020004 | 143.949997 | 84.430000 | 211.397858 | 335.096588 | 232.589996 | 2021-10-01 |
| 10 | 243.251434 | 109.169281 | 2849.040039 | 3507.070068 | 329.986298 | 158.830002 | 44.256985 | 63.700001 | 147.559998 | 135.070007 | 69.820000 | 193.429489 | 314.920013 | 184.889999 | 2021-11-01 |
In [ ]:
df_long=pd.melt(df_n , value_vars=df.columns,id_vars='Fecha')
df_long
Out[ ]:
| Fecha | variable | value | |
|---|---|---|---|
| 0 | 2021-01-01 | MCD | 203.187836 |
| 1 | 2021-02-01 | MCD | 201.525894 |
| 2 | 2021-03-01 | MCD | 220.471207 |
| 3 | 2021-04-01 | MCD | 232.215775 |
| 4 | 2021-05-01 | MCD | 230.061630 |
| ... | ... | ... | ... |
| 149 | 2021-07-01 | PYPL | 275.529999 |
| 150 | 2021-08-01 | PYPL | 288.660004 |
| 151 | 2021-09-01 | PYPL | 260.209991 |
| 152 | 2021-10-01 | PYPL | 232.589996 |
| 153 | 2021-11-01 | PYPL | 184.889999 |
154 rows × 3 columns
In [ ]:
import plotly.express as px
fig=px.line(data_frame=df_long,x='Fecha',y='value',line_group='variable',color='variable',title='Comportamiento de precio de acciones',\
labels={
"Fecha": "Fecha_dias",
"value": "Precio (USD)"
})
fig.update_layout(paper_bgcolor="#FFFFFF",plot_bgcolor='#FFFFFF',)
fig.show()
Excluyendo Amazon y GOOGLE¶
In [ ]:
df_n=df.copy()
df_n['Fecha']=df.index
df_n= df_n.reset_index(drop=True)
df_n= df_n.drop(columns=['GOOG','AMZN'])
df_n
Out[ ]:
| MCD | SBUX | MSFT | JPM | BAC | C | MAR | HLT | RCL | V | MA | PYPL | Fecha | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 203.187836 | 95.235695 | 230.031799 | 125.517960 | 29.089981 | 56.271671 | 116.309998 | 101.389999 | 65.000000 | 192.070175 | 314.746094 | 234.309998 | 2021-01-01 |
| 1 | 201.525894 | 106.273232 | 230.448318 | 144.598679 | 34.054409 | 64.472809 | 148.070007 | 123.680000 | 93.269997 | 211.093323 | 352.569061 | 259.850006 | 2021-02-01 |
| 2 | 220.471207 | 107.950699 | 234.348633 | 149.570267 | 37.959236 | 71.196068 | 148.110001 | 120.919998 | 85.610001 | 210.764023 | 354.761108 | 242.839996 | 2021-03-01 |
| 3 | 232.215775 | 113.107674 | 250.659698 | 151.122665 | 39.961990 | 69.718323 | 148.520004 | 128.699997 | 86.949997 | 232.494446 | 380.676971 | 262.290009 | 2021-04-01 |
| 4 | 230.061630 | 112.505043 | 248.174774 | 162.320847 | 41.795921 | 77.564484 | 143.580002 | 125.269997 | 93.269997 | 226.263016 | 359.701202 | 260.019989 | 2021-05-01 |
| 5 | 228.463959 | 110.899529 | 269.888611 | 153.722504 | 40.652176 | 69.720337 | 136.520004 | 120.620003 | 85.279999 | 233.091354 | 364.200226 | 291.480011 | 2021-06-01 |
| 6 | 240.055786 | 120.441185 | 283.846313 | 150.006454 | 37.981617 | 66.635887 | 145.979996 | 131.449997 | 76.870003 | 245.622177 | 384.999420 | 275.529999 | 2021-07-01 |
| 7 | 234.863205 | 116.533272 | 300.752960 | 158.991928 | 41.338177 | 71.392593 | 135.139999 | 124.860001 | 82.730003 | 228.386063 | 345.793121 | 288.660004 | 2021-08-01 |
| 8 | 239.780670 | 109.836403 | 281.405182 | 162.709534 | 42.031273 | 69.675041 | 148.089996 | 132.110001 | 88.949997 | 222.358551 | 347.241272 | 260.209991 | 2021-09-01 |
| 9 | 244.196198 | 105.614609 | 331.014404 | 168.872391 | 47.551128 | 68.662384 | 160.020004 | 143.949997 | 84.430000 | 211.397858 | 335.096588 | 232.589996 | 2021-10-01 |
| 10 | 243.251434 | 109.169281 | 329.986298 | 158.830002 | 44.256985 | 63.700001 | 147.559998 | 135.070007 | 69.820000 | 193.429489 | 314.920013 | 184.889999 | 2021-11-01 |
In [ ]:
columnas=list(df.columns)
unwanted_num = {'AMZN', 'GOOG'}
col_final = [x for x in columnas if x not in unwanted_num]
col_final
Out[ ]:
['MCD', 'SBUX', 'MSFT', 'JPM', 'BAC', 'C', 'MAR', 'HLT', 'RCL', 'V', 'MA', 'PYPL']
In [ ]:
df_long=pd.melt(df_n , value_vars=col_final,id_vars='Fecha')
df_long
Out[ ]:
| Fecha | variable | value | |
|---|---|---|---|
| 0 | 2021-01-01 | MCD | 203.187836 |
| 1 | 2021-02-01 | MCD | 201.525894 |
| 2 | 2021-03-01 | MCD | 220.471207 |
| 3 | 2021-04-01 | MCD | 232.215775 |
| 4 | 2021-05-01 | MCD | 230.061630 |
| ... | ... | ... | ... |
| 127 | 2021-07-01 | PYPL | 275.529999 |
| 128 | 2021-08-01 | PYPL | 288.660004 |
| 129 | 2021-09-01 | PYPL | 260.209991 |
| 130 | 2021-10-01 | PYPL | 232.589996 |
| 131 | 2021-11-01 | PYPL | 184.889999 |
132 rows × 3 columns
In [ ]:
import plotly.express as px
fig=px.line(data_frame=df_long,x='Fecha',y='value',line_group='variable',color='variable',title='Comportamiento de precio de acciones',\
labels={
"Fecha": "Fecha_dias",
"value": "Precio (USD)"
})
fig.update_layout(paper_bgcolor="#FFFFFF",plot_bgcolor='#FFFFFF',)
fig.show()
Ahora se pueden ver mucho mejor las fluctuaciones de cada compañia que antes no era tan claro
Created in