efueyo's questions

I have the following DataFrame.

import pandas as pd

lista = [['Producto1', '06/22/2012', 66.72721, 17.995],
 ['Producto1', '09/18/2017', 240.56891, 19.244],
 ['Producto1', '01/08/2018', 219.24285, 17.459],
 ['Producto1', '03/06/2018', 667.32977, 18.245],
 ['Producto2', '10/07/2018', 49.328, 254.64],
 ['Producto3', '27/04/2016', 733.27266, 13.7643],
 ['Producto3', '06/12/2019', 1213.05103, 13.71],
 ['Producto4', '10/08/2012', 44986.57615, 10.4718] ]

df_aux = pd.DataFrame(lista)
df_aux.columns = ["Producto", "Fecha", "Cantidad", 'Precio']
df_aux['Total'] = df_aux['Cantidad'] * df_aux['Precio']
df_aux

Bring back.

    Producto    Fecha   Cantidad    Precio  Total
0   Producto1   06/22/2012  66.72721    17.9950     1200.756144
1   Producto1   09/18/2017  240.56891   19.2440     4629.508104
2   Producto1   01/08/2018  219.24285   17.4590     3827.760918
3   Producto1   03/06/2018  667.32977   18.2450     12175.431654
4   Producto2   10/07/2018  49.32800    254.6400    12560.881920
5   Producto3   27/04/2016  733.27266   13.7643     10092.984874
6   Producto3   06/12/2019  1213.05103  13.7100     16630.929621
7   Producto4   10/08/2012  44986.57615     10.4718     471090.4281

The DataFrame has four columns: 'Products', 'Dates', 'Quantity', 'Price', 'Total' I want to group by products, and obtain a DataFrame with only two columns, the sum of 'Quantity' and 'Total. I execute

df_productos = df_aux.groupby(by= ['Producto']).sum( )
df_productos

Which brings me back

    Cantidad    Precio  Total
Producto            
Producto1   1193.86874  72.9430     21833.456820
Producto2   49.32800    254.6400    12560.881920
Producto3   1946.32369  27.4743     26723.914495
Producto4   44986.57615     10.4718     471090.428128

With the groupiby() method, can I get it not to return (add) the prices column? I will appreciate suggestions.

I show below a script that imports historical quotes of different stocks from Yahoo, using 'pandas_datareader' .

# 1 - Define `tickers` & `company names` for every instrument
stocks      = {'AAPL':'Apple', 'MSFT':'Microsoft', 'AMZN' : 'Amazon',  'GOOG': 'Google', 'FB':'Facebook','NFLX':'Netflix' ,  'NVDA' : 'NVIDIA'}
bonds       = {'HCA' : 'HCA', 'VRTX' :  'VRTX'}
commodities = {'BTC-USD' : 'Bitcoin', 'PA=F' : 'Palladium'}
instruments = {**stocks, **bonds, **commodities}
tickers     = list(instruments.keys())
instruments_data = {}
N = len(tickers)


# 2 - We will look at stock prices over the past years, starting at January 1, 2015
#                               01-01-2015 - 16-04-2020
start = datetime.datetime(2015,1,1)
end   = datetime.datetime(2020,4,16)

# 3 - Let's get instruments data based on the tickers.
# First argument is the series we want, second is the source ("yahoo" for Yahoo! Finance), third is the start date, fourth is the end date
for ticker, instrument in instruments.items():
  print("Loading data series for instrument {} with ticker = {}".format(instruments[ticker], ticker))
  instruments_data[ticker] = web.DataReader(ticker, data_source = 'yahoo', start = start, end = end)

The statement instruments = {**stocks, **bonds, **commodities}, I guess creates a dictionary with three dictionaries. I don't understand what function the two '**' have. I will be grateful if you explain this sentence to me.

I import the following quotes into a DataFrame.

import pandas as pd
import numpy as np
from pandas_datareader import data as pdr
import yfinance as yf
yf.pdr_override() # <== that's all it takes :-)

# download dataframe
datos = pdr.get_data_yahoo("SPY", start="2000-01-01", end="2021-04-30")
datos.shape[0]

The statement datos.shape[0]returns the number of records in the DataFrame, which in this case is 5366. Next I convert the DataFrame to a dictionary and try to get it to return the number of elements in the dictionary.

data = datos.to_dict()
len(data)

It returns me 6, which is wrong. What is my fault? I will appreciate help.

The theory teaches that "composition" is not deriving one class from another, but rather injecting it with an object that is an instance of the class it needs. The class stores that object internally and from there you can safely use it. I try to do it in this example, and I just can't get it right.

# creamos la clase Cuenta
class Cuenta:
    # inicializamos los atributos de la clase
    def __init__(self,titular,cantidad):
        self.titular=titular
        self.cantidad=cantidad
 
    # imprimimos los datos
    def imprimir(self):
        print("Titular: ",self.titular)
        print("Cantidad: ", self.cantidad) 

#****************************************************************
        
# creamos la clase CajaAhorro
# esta clase hereda atributos de la clase Cuenta
class CajaAhorro():
    # iniciamos los atributos de la clase
    def __init__(self,titular,cantidad, cuenta):
        self.cuenta = cuenta
 
    # imprimimos los datos de la cuenta
    def imprimir_datos_cuenta(self):
        print("Cuenta de caja de ahorros")
        super().imprimir()

# *****************************************************************

# creamos la clase PlazoFijo
# esta clase también hereda atributos de la clase Cuenta
class PlazoFijo():
    # inicializamos los atributos de la clase
    def __init__(self,titular,cantidad,plazo,interes, caja_ahorro):
        self.caja_ahorro = caja_ahorro
        self.plazo=plazo
        self.interes=interes 
 
    # calculamos la ganancia
    def ganancia(self):
        ganancia=self.cantidad*self.interes/100
        print("El importe de interés es: ",ganancia)
 
 
    # imprimimos los resultados
    def imprimir(self):
        print("Cuenta a plazo fijo")
        super().imprimir()
        print("Plazo disponible en días: ",self.plazo)
        print("Interés: ",self.interes)
        self.ganancia()
 
 
# bloque principal
cuenta = CajaAhorro("Manuel",5000)
caja_ahorro = CajaAhorro(cuenta)
plazo_fijo = PlazoFijo(caja_ahorro)

plazo_fijo.imprimir_datos_cuenta()

I returned:

---------------------------------------------------------------------------
TypeError                                 Traceback (most recent call last)
<ipython-input-17-2c3c3943cb45> in <module>
     52 
     53 # bloque principal
---> 54 cuenta = CajaAhorro("Manuel",5000)
     55 caja_ahorro = CajaAhorro(cuenta)
     56 plazo_fijo = PlazoFijo(caja_ahorro)

TypeError: __init__() missing 1 required positional argument: 'cuenta'

I will appreciate help to clarify and fix this concept.

I am trying to transition from jupyter to jupyter lab2.0. I have installed the package in a fresh environment. When trying to install some of the extensions that come by default in the package, I always get the error

"@jupyterlab/translation" is not a valid npm package

The error is reproduced with any of the extensions that come by default.

I have installed: python 3.8.5, JupyterLab v2.2.6 andnodejs 10.13.0

On the other hand, how can you upgrade to jupyter lab 3? The option indicated by jupyterlab, conda conda install -c conda-forge jupyterlab has installed version 2.

What can be the cause? How can i solve this problem?. I will appreciate help.

Getting started on plotly. I import from Yahoo, quotes of two values ​​doing:

import pandas as pd
import pandas_datareader as pdr 

dicc_tickers = {"Apple":'AAPL', "Google":'GOOG'}

from pandas_datareader import data as pdr
import datetime

def get(tickers, startdate, enddate):
    def data(ticker):
        return (pdr.DataReader(ticker, 'yahoo' ))
    datas = map (data, tickers)
    return(pd.concat(datas, keys=tickers, names=['Ticker', 'Date']))

startdate = "2016-1-4"
enddate = "2020-10-30"
tickers = dicc_tickers.values()
cotizaciones = get(tickers, datetime.datetime(2006, 10, 1), datetime.datetime(2012, 1, 1))
datos_yahoo = cotizaciones[['Adj Close']].reset_index().pivot('Date', 'Ticker', 'Adj Close')
df = datos_yahoo.resample('M').mean()

Below I try to present the graphs of the historical evolution of these average monthly prices. I need a date scale on the X axis, executing the following script:

import chart_studio.plotly as ply
import plotly.graph_objs as go
import numpy as np

df = datos_yahoo[["AAPL", "GOOG"]]["2016-1-4":"2020-11-15"].resample('M').mean()

x = df.index.values
y1 = df["AAPL"]
y2 = df["GOOG"]
trace1 = go.Scatter(
   x = x,
   y = y1,
   name = 'AAPL'
)
trace2 = go.Scatter(
   x = x,
   y = y2,
   name = 'GOOG',
   yaxis = 'y2'
)
data = [trace1, trace2]
layout = go.Layout(
   title = 'Double Y Axis Example',
   yaxis = dict(
      title = 'AAPL',zeroline=True,
      showline = True
   ),
   yaxis2 = dict(
      title = 'GOOG',
      zeroline = True,
      showline = True,
      overlaying = 'y',
      side = 'right'
   )
)
fig = go.Figure(data=data, layout=layout)
iplot(fig)

To do this, I define the variable x: x = df.index.values But I can't get the date scale to appear on the x axis. How can I get this scale on the chart? I will appreciate your suggestions.

In the following DataFrame, the values ​​(1,531.20,...) are imported in the format object.

I try to convert them to the type float, applying the method .astype(float).

# Create DF from dict of lists/ndarrays
df = pd.DataFrame({'MSCI' : ['1,448.94', '1,480.57', '1,495.18', '1,531.20', '1,532.28'],
                       'Date' : ['2012-07-09','2012-07-10', '2012-07-11', '2012-07-12', '2012-07-13']})

df.set_index("Date", inplace = True)
df["MSCI"] = df.MSCI.astype(float)
df.info() 

It returns me the error:

ValueError: could not convert string to float: '1,448.94'

I will appreciate suggestions to correct this error-

On the other hand, could I somehow get these numbers to be converted to the format on import float?

In the import I use pd.read_csv.

I don't understand this way to initialize a multidimensional array using a list of lists (Numpy):

np.array([range(i, i + 3) for i in [2, 4, 6]])

OUT:

array([[2, 3, 4],
       [4, 5, 6],
       [6, 7, 8]])

How do you get 3, 5, 7 and 4, 6, 8?

I understand that with range(i, i+3) you are defining that the matrix will have three columns, but how can we understand in this code, that the values ​​of each of the two columns are obtained by adding 1 to the value of the previous column ?.

I have the dataframe

            Acatis  TrueValue  ValorRelativo
Date                                        
2015-12-29  220.43     12.959         12.311
2020-05-13  270.27     12.870         13.234

I need to transform it into this other one.

          Nombre        Date    Close
0         Acatis  2015-12-29  220.430
1         Acatis  2020-05-13  270.270
2      TrueValue  2015-12-29   12.959
3      TrueValue  2020-05-13   12.870
4  ValorRelativo  2015-12-29   12.311
5  ValorRelativo  2020-05-13   13.234

Is it possible to do it by applying some method/s?

Indeed, using "melt" it can be done like this:

p1 = pd.melt(df_analisis, id_vars =['Date'], value_vars =['Acatis']) 
p2 = pd.melt(df_analisis, id_vars =['Date'], value_vars =['TrueValue']) 
p3 = pd.melt(df_analisis, id_vars =['Date'], value_vars =['ValorRelativo']) 

datos  = pd.concat([p1, p2, p3], join="inner")
datos

The output is:

        Date       variable    value
0 2015-12-29         Acatis  220.430
1 2020-05-13         Acatis  270.270
0 2015-12-29      TrueValue   12.959
1 2020-05-13      TrueValue   12.870
0 2015-12-29  ValorRelativo   12.311
1 2020-05-13  ValorRelativo   13.234

Is there a more direct way?

As an example, I attach the code to draw the graph of a normal distribution. I have managed to fill its entire interior with color, but what I really want is to fill only from the extreme left to the ordered one marked with the letter "a".

import pandas as pd
from scipy import stats
import matplotlib.pyplot as plt # importando matplotlib
# Graficando Normal

etiquetas = "Z"
x_pos = [1]
mu, sigma = 0, 1 # media y desvio estandar
normal = stats.norm(mu, sigma)
x = np.linspace(normal.ppf(0.01),
                normal.ppf(0.99), 100) # función e densidad de probabilidad

fp = normal.pdf(x) # Función de Probabilidad
plt.plot(x, fp)
plt.xticks(x_pos, etiquetas) 
plt.fill_between(x  , fp, facecolor="green", alpha= 0.2)
plt.vlines(1, ymin= 0,  ymax= 0.25, color = "blue", linestyles ="--")
plt.title('Distribución Normal')
plt.ylabel('probabilidad')
plt.xlabel('valores')
plt.show()

I will appreciate your suggestions.

I have the following dataframe. Dataframe with observation data.

import numpy as np
data = np.array([[15, 38, 14, 13, 29, 25], [20, 13, 16, 32, 44, 39], [45, 46, 19, 23, 24, 18], 
                 [19, 20, 21, 18, 25, 33], [13, 18, 22, 24, 27, 27] ])
# Creating pandas dataframe from numpy array
datos = pd.DataFrame({'Column1': data[:, 0], 'Column2': data[:, 1], 'Column3': data[:, 2], 'Column4': data[:, 3], 'Column5': data[:, 4]})


# Deuelve una lista por cada fila
lista_datos = datos.values.tolist()

# Ordenación de los datos.
lista_ordenada_ventas = np.sort(lista_datos, axis=None)

# Crear un narray con una lista
array_datos_ordenados = np.array(lista_ordenada_ventas.reshape(datos.shape[0], datos.shape[1]))

# Crear un df con un narray
ventas_semanales_ordenadas = pd.DataFrame(array_datos_ordenados)

# Crear un df de una única columna, con una lista
ventas =  pd.DataFrame(lista_ordenada_ventas)  
ventas.columns = (["Valores"])

I create a DataFrame with the limits of the class intervals applicable to said observations for analysis.

datos = np.zeros((6, 2))
intervalos = pd.DataFrame(datos, columns = ["LimInf", "LimSup"] )

intervalos.iloc[0,0] = 13
c = 6
intervalos.iloc[0, 1] = 13 + c -1
for i in range (1, intervalos.shape[0]):
    intervalos.iloc[i,0] = intervalos.iloc[i-1,1] + 1
    intervalos.iloc[i, 1] = intervalos.iloc[i,0] + c -1
intervalos

We continue to create exact limits,

intervalos["LimExacInf"] = 0
intervalos["LimExacSup"] = 0
# Calcula el límite exacto superior
for i in range (0, intervalos.shape[0]):
    intervalos.loc[i , "LimExacSup"]  = (intervalos.loc[i, "LimSup"]  + intervalos.loc[i, "LimInf"])/2
for i in range (1, intervalos.shape[0]):
    intervalos.loc[i , "LimExacInf"]  = intervalos.loc[i-1, "LimExacSup"] 

c = intervalos.loc[1, "LimExacSup"] - intervalos.loc[1, "LimExacInf"] 
intervalos.loc[0, "LimExacInf"] = intervalos.loc[0, "LimInf"] 
intervalos["MarcaClase"] = (intervalos["LimExacSup"] + intervalos["LimExacInf"]) / 2
intervalos

Next I want to add the "AbsoluteFrequency" column, counting in the table of variables how many are included within each range, which meet the condition of being >= the lower limit of the interval or < than the upper limit. I try with this script, and it gives me an error that I can't quite interpret.

frecuencia = 0
intervalos["FrecAbsolutas"]= 0
lista_frecuencias= [0]
for i in range (0, ventas.shape[0]):
    for j in range (0, intervalos.shape[0]): 
        if ventas.iloc[i,0] >= intervalos.iloc[j, 3] or ventas.iloc[i,0] < intervalos.iloc[j, 4]:
            lista_frecuencias[i] = lista_frecuencias[i] + 1

intervalos["FrecAbsolutas"]= lista_frecuencias

intervalos

The absolute frequencies should be: 5, 8, 5, 2, 1 and 3 Returns the error: IndexError: list index out of range I would appreciate suggestions to modify the script.

On the other hand, will there be any function in pandas, scipy, etc, that performs this task? I will appreciate your help.

I have the following "table" dataframe:

    0   1   2   3   4   5   6   7   8   9   10  11  12  13  14  15  16  17  18  19
0   75  28  74  76  38  82  28  90  51  54  64  61  48  70  59  52  84  45  60  30
1   49  54  87  18  66  63  56  23  59  100 69  59  24  33  61  15  29  33  75  74
2   45  66  41  51  87  49  63  41  51  35  95  53  40  22  29  76  34  19  48  75
3   60  69  73  75  60  56  52  74  88  38  66  30  49  56  45  59  79  61  67  27

We prepare it for the expected calculations, as follows.

import pandas as pd

# Ordenación de los datos
datos = np.sort(tabla, axis=None)
lista_valores = tabla.values.tolist()
lista_ordenada = np.sort(tabla, axis=None)
# Create ndArray from a list
array_datos_ordenados = np.array(lista_ordenada.reshape(4,20))
# Generamos el dataframe valores
valores =  pd.DataFrame(lista_ordenada)
valores.columns = (["Valores"])
valores_ordenados = pd.DataFrame(array_datos_ordenados)

Using these tables and lists, I calculate various measures of the descriptive statistics, using the equivalent functions of pandas, numpy and python statistics. I notice the following differences in some results, for example. Standard deviation.

# Con pandas. Cálculo sobre una columna
valores["Valores"].std()

Returns 19.925494133288044

# Con pandas. Cálculo sobre una serie
lista_ordenada.std()

Returns 19.80056817366613

# Desviación estádard
import numpy as np
np.std(lista_ordenada)

Returns 19.80056817366613

variance

# Con pandas. Cálculo sobre una columna
valores["Valores"].var()

Returns 397.0253164556962

import statistics as st
st.variance(lista_ordenada)

returns 397

import numpy as np
np.var(lista_ordenada)

Returns 392.0625. What could be the reason for these differences, and which of these options should we consider more accurate?

The Python package called Patsy helps to define a formula with dependent and independent variable. The variable defined to the left of '~'is the dependent variable, and the variables defined to the right of it are the independent variables. Variables enclosed within C() are treated as categorical variables. From the DataFrame "titanic_3.csv", I need to create two sets: training set and testing set. This DataFrame is accessible in the Files folder of this link

When executing the script

import os
import pandas as pd
from patsy import dmatrices
# Change directory 
os.chdir("G:\Py_machine_learning\Ficheros")
df = pd.read_csv("titanic_3.csv")
# Aplicar axis as 1 para eliminar las columnas con las siguientes etiquetas
df = df.drop(["ticket","cabin","name", "Unnamed: 14", "Unnamed: 15", "Unnamed: 16",
              "Unnamed: 17", "Unnamed: 18", "Unnamed: 19"], axis=1)
# Eliminamos valores Na
df = df.dropna()
from patsy import dmatrices
formula = "survived ~ C(pclass) + C(sex) + age + sibsp + C(embarked) + parch" 
# Crea un diccionario de resultados para mantener nuestros resultados de regresión 
# para un fácil análisis posterior
df_train = df.iloc[ 0: 600, : ]
df_test = df.iloc[ 600: , : ]
# Divide los datos en variables dependientes e independientes.
# Crea los conjuntos de datos de entrenamiento y prueba
y_train,x_train = dmatrices(formula, data=df_train, return_type = 'dataframe')
y_test,x_test = dmatrices(formula, data=df_test,  return_type = 'dataframe')

it returns me the error:

ValueError: negative dimensions are not allowed What is the cause of this error?

I have the following dataframe:

data.info()
<class 'pandas.core.frame.DataFrame'>
DatetimeIndex: 658 entries, 2016-01-04 to 2019-01-31
Data columns (total 4 columns):
Equity    658 non-null float64
Equity    658 non-null float64
Equity    658 non-null float64
Equity    658 non-null float64
dtypes: float64(4)
memory usage: 25.7 KB

I try to rename the columns.

old_names = ['Equity', 'Equity', 'Equity', 'Equity' ] 
new_names = ['Equity1','Equity2', 'Equity3','Equity4' ]
data.rename(columns=dict(zip(old_names, new_names)), inplace=True)

It returns the error.

---------------------------------------------------------------------------
TypeError                                 Traceback (most recent call last)
<ipython-input-123-9f8bfefe3429> in <module>
      1 old_names = ['Equity', 'Equity', 'Equity', 'Equity' ]
      2 new_names = ['Equity1','Equity2', 'Equity3','Equity4' ]
----> 3 data.rename(columns=dict(zip(old_names, new_names)), inplace=True)

TypeError: 'dict' object is not callable

What can be the cause?. I will appreciate your help.

I have a dataframe df with the following structure

            Acatis  Avantage  TrueValue  ValorRelativo
Date                                                  
2014-07-31  199.08      10.0      10.22         11.631
2014-08-04  198.79      10.0      10.19         11.616
2014-08-05  198.59      10.0      10.17         11.614

I need to make, with each column, a dataframe whose name is the name of the column and whose name of the only column is "Close". Each of these dataframes must have this structure.

             Close
Date              
2014-07-31  199.08
2014-08-04  198.79
2014-08-05  198.59

I can do the following:

acatis = df[["Acatis"]].rename (columns = {"Acatis":"Close"})

but it solves the problem only partially, because the name of the new dataframe, acatis in this example, I have to write, that is, it is not associated by default in some way. On each execution of the program I can select another list which will result in other different names and I would have to modify the names of these new df's. I've tried pivot(), stack(), and unstack() and still can't find a solution. What can be the easiest way to do it? I will appreciate your help

How can all the columns of a DataFrame be displayed on the screen? In my case, the execution indicates me with ellipses, the columns not shown.

                   EPSV  Acatis  Avantage   ...      Nasdaq  Nikkei_225  \

Date ...
2019-01-30 00:00:00 11.914 249.57 13.258 ... 7183.08 20556.539
2019-01-31 00:00:00 11.951 250.05 13.236 ... 7281.74 20773.490 2019-0:
00 11.954 250.90 13.272 ... 7263.87 20788.391
I will appreciate your help

I have the following DataFrame

d = {'Ticker': ["Total_Pond", "Total_Pond", "Total_Pond", "ValorRelativo", "Avantage","Acatis" ],
        'Tipo_Inv': ["FI", "FI", "FI", "FI","FI", "FI"], 'Fact_Pond': [2.313857e+06, 1.342821e+06,3.486820e+06,2.788324e+06, 4.929096e+06, 2.663564e+06],
        'Total_Pond': [-53196.922835, -37853.091155, -114935.107120, -26013.111409,374923.428199, -235590.992103 ]}
df = pd.DataFrame(data=d)
df

I try to calculate the weighted average interest of each value, for which I pivot the table and add the column df_Imp_value with the result of the division ["Total_Pond"] by df ["Fact_Pond"]

# Interes medio ponderado de cada valor
df_Imp_valor = pd.pivot_table(df,index=["Ticker"],values=['Fact_Pond', 'Total_Pond' ],aggfunc=np.sum)
df_Imp_valor["Int_Med_Pond_Val"] = df ["Total_Pond"]/  df ["Fact_Pond"]
df_Imp_valor[:3]

The result is not correct, since it adds NaN to the value of that column.

          Fact_Pond     Total_Pond     Int_Med_Pond_Val
Ticker          
Acatis    2663564.0     -235590.992103    NaN
Avantage    4929096.0   374923.428199      NaN
Total_Pond  7143498.0   -205985.121110     NaN

I check in another way, that the result of the division is a number

a = df ["Total_Pond"]
b = df ["Fact_Pond"] 
a/b

0   -0.022991
1   -0.028189
2   -0.032963
3   -0.009329
4    0.076063
5   -0.088450
dtype: float64

Surely I am obsessed. What is the mistake I make? Thanks for the help

I have the following DataFrame

The following statement should correctly filter the values ​​between two dates.

# filtrando sólo del 2018-07-26 al 2018-08-02
df_selec['2018-07-26':'2018-08-02']

However, it returns me

Empty DataFrame
Columns: [cierres, Retornos, Retornos_acum, var_diaria, rend_diario, tend, prod_mov, pronostico, MA7, MA30, MA90, MA120]
Index: []

This another way to filter

# Seleccionar entre dos fechas del índice 
df_selec[(df_selec['Date'] > '2018-07-30') & (df_selec['Date'] < '2018-08-02')]

I returned

KeyError: 'Date'

If we now do the same with a DataFrame obtained from Yahoo, the selection is made correctly

# Creando una serie de tiempo de las acciones de WFT desde yahoo finance
wft = web.DataReader("WFT", 'yahoo', '2000-1-1', '2018-8-30')
# filtrando sólo del 2016-02-04 al 2016-02-18
wft['2016-02-04':'2016-02-18']

Where is my mistake? What is it that I don't understand?

I need to add data from other dataframes to a dataframe, using the merge function, to ensure that it only takes those values ​​whose indices have the same date. The statement that I show below returns the error mentioned in the Title.

df_total = pd.merge(cotiz_diaria, [df, df1, df2, df3, df4, df5, df6, df7, df8, df9, df10, df11, df12], left_index=True, right_index=True) 
df_df_total

The error seems to indicate that it doesn't support df lists.

How else could I do this join, other than adding each dataframe one by one?

After importing using urlibInternet data, I manage to create a DataFrame (df)with two columns (Fecha y Valor)of the types:

Fecha    1563 non-null object, 
Valor    1563 non-null object.

With the statement df["Fecha"] = pd.to_datetime(df["Fecha"])I get to pass the values ​​of the column Fecha, to the formatFecha 1563 non-null datetime64[ns]

Next I try to change the format of the column valorwith the statement df["Valor"] = pd.to_numeric(df["Valor"])and I get the error:

ValueError: Unable to parse string "185,130000" at position 0.

I try again with the statement df['Valor'] = df['Valor'].apply(np.float)and it gives me the error:

ValueError: Unable to parse string "185,130000" at position 0

What else could I do?