In [ ]:

x = 15 + 1.3
  
print(x)

16.3

In [ ]:

x = 40
y = 12
  
add = x + y
sub = x - y
pro = x * y
div = x / y
  
print(add)
print(sub)
print(pro)
print(div)

52
28
480
3.3333333333333335

In [ ]:

a = 13
b = 12.0
  
c = a + int(b)
print(c)

In [ ]:

a = 13
b = 5
  
c = a / b
print(c)

2.6

Desafio generico¶

Lectura de informacion¶

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

Parte 1¶

Analizando el comportamiento de una serie de tiempo¶

In [ ]:

df['GOOG'].plot(kind='line',figsize=(10,6),xlabel='Fecha', ylabel='Precio Accion', title='Precio Accion vs Fecha')

Out[ ]:

<matplotlib.axes._subplots.AxesSubplot at 0x7f1ac1f74f10>

Se observa un comportamiento creciente entre el 01/01/21 hasta el 11/01/2021

Extraccion usando indices booleanos¶

In [ ]:

columnas=list(df.columns)
google= [x for x in columnas if x =='GOOG']
google

In [ ]:

indice_col=list(df.columns=='GOOG')
df_goog=df.loc[:,indice_col]
df_goog # Hemos terminado

Out[ ]:

	GOOG
formatted_date
2021-01-01	1835.739990
2021-02-01	2036.859985
2021-03-01	2068.629883
2021-04-01	2410.120117
2021-05-01	2411.560059
2021-06-01	2506.320068
2021-07-01	2704.419922
2021-08-01	2909.239990
2021-09-01	2665.310059
2021-10-01	2965.409912
2021-11-01	2849.040039

Grafico interactivo¶

In [ ]:

df_n= df_goog.copy()
df_n['Fecha']= df_n.index 
df_n= df_n.reset_index(drop=True)
df_n['Fecha']=pd.to_datetime(df_n['Fecha'])
df_n

Out[ ]:

	GOOG	Fecha
0	1835.739990	2021-01-01
1	2036.859985	2021-02-01
2	2068.629883	2021-03-01
3	2410.120117	2021-04-01
4	2411.560059	2021-05-01
5	2506.320068	2021-06-01
6	2704.419922	2021-07-01
7	2909.239990	2021-08-01
8	2665.310059	2021-09-01
9	2965.409912	2021-10-01
10	2849.040039	2021-11-01

In [ ]:

import plotly.express as px


fig=px.line(data_frame=df_n,x='Fecha',y='GOOG',title='Comportamiento GOOGLE',\
             labels={
                     "Fecha": "Fecha_dias",
                     "value": "Precio (USD)"
                 })
fig.update_layout(paper_bgcolor="#FFFFFF",plot_bgcolor='#FFFFFF',)

fig.show()

Parte 2¶

In [ ]:

df_n

Out[ ]:

	GOOG	Fecha
0	1835.739990	2021-01-01
1	2036.859985	2021-02-01
2	2068.629883	2021-03-01
3	2410.120117	2021-04-01
4	2411.560059	2021-05-01
5	2506.320068	2021-06-01
6	2704.419922	2021-07-01
7	2909.239990	2021-08-01
8	2665.310059	2021-09-01
9	2965.409912	2021-10-01
10	2849.040039	2021-11-01

In [ ]:

pasos = 2
def my_fun(x):
    return x.iloc[-1] - x.iloc[0]

df_n['Dif']=df_n['GOOG'].rolling(window=pasos).apply(my_fun) # Hacemos la diferencia del valor actual - anterior
df_n

Out[ ]:

	GOOG	Fecha	Dif
0	1835.739990	2021-01-01	NaN
1	2036.859985	2021-02-01	201.119995
2	2068.629883	2021-03-01	31.769897
3	2410.120117	2021-04-01	341.490234
4	2411.560059	2021-05-01	1.439941
5	2506.320068	2021-06-01	94.760010
6	2704.419922	2021-07-01	198.099854
7	2909.239990	2021-08-01	204.820068
8	2665.310059	2021-09-01	-243.929932
9	2965.409912	2021-10-01	300.099854
10	2849.040039	2021-11-01	-116.369873

In [ ]:

# HAcemos un filtro de cuando los valores de Dif <0
index_bool= df_n.Dif<0
index_bool

Out[ ]:

0     False
1     False
2     False
3     False
4     False
5     False
6     False
7     False
8      True
9     False
10     True
Name: Dif, dtype: bool

In [ ]:

# Ahora podemos aplicar el filtro sobre el objeto data frame
df_neg=df_n.loc[index_bool,:]
df_neg

Out[ ]:

	GOOG	Fecha	Dif
8	2665.310059	2021-09-01	-243.929932
10	2849.040039	2021-11-01	-116.369873

Solo existieron dos días donde la accion bajo de precio

Graficando fechas donde ocurrio la bajada¶

In [ ]:

import matplotlib.pyplot as plt
fig,ax= plt.subplots(figsize=(10,6))
ax.plot(df_n.Fecha,df_n.GOOG)
ax.scatter(x=df_neg.Fecha, y= df_neg.GOOG, s=20, color='red', label='R')
ax.set_xlabel('Fecha')
ax.set_ylabel('Precio')
ax.set_title('Precio Accion vs tiempo')
ax.legend(loc='upper left')

Out[ ]:

<matplotlib.legend.Legend at 0x7f1ab9329b10>

Desafio¶

Intenten hacer le mismo procedimiento para AMZN y observen si existe el mismo patron

Con esto entonces podemos monitorear cuando una accion puede disminuir su valor

In [ ]:

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