Ошибки вычисления среднего значения pandas

I’m trying to implement some machine learning algorithms, but I’m having some difficulties putting the data together.

In the example below, I load a example data-set from UCI, remove lines with missing data (thanks to the help from a previous question), and now I would like to try to normalize the data.

For many datasets, I just used:

valores = (valores - valores.mean()) / (valores.std())

But for this particular dataset the approach above doesn’t work. The problem is that the mean function is returning inf, perhaps due to a precision issue. See the example below:

bcw = pd.read_csv('http://archive.ics.uci.edu/ml/machine-learning-databases/breast-cancer-wisconsin/breast-cancer-wisconsin.data', header=None)

for col in bcw.columns:
    if bcw[col].dtype != 'int64':
        print "Removendo possivel '?' na coluna %s..." % col
        bcw = bcw[bcw[col] != '?']

valores = bcw.iloc[:,1:10]
#mean return inf
print  valores.iloc[:,5].mean()

My question is how to deal with this. It seems that I need to change the type of this column, but I don’t know how to do it.

Errorbar is the plotted chart that refers to the errors contained in the data frame, which shows the confidence & precision in a set of measurements or calculated values. Error bars help in showing the actual and exact missing parts as well as visually display the errors in different areas in the data frame. Error bars are the descriptive behavior that holds information about the variances in data as well as advice to make proper changes to build data more insightful and impactful for the users.

Here we discuss how we plot errorbar with mean and standard deviation after grouping up the data frame with certain applied conditions such that errors become more truthful to make necessary for obtaining the best results and visualizations. 

Modules Needed:

pip install numpy
pip install pandas
pip install matplotlib

Here is the DataFrame from which we illustrate the errorbars with mean and std: 
 

Output:

groupby the subplots with mean and std to get error bars: 

 
 Output:

Now we see error bars using NumPy keywords of mean and std:

 
 Output:

By the above example, we can see that errors in poor quality are higher than good instead of more good values in the data frame.

Now, we move with another example with data frame below:

 By the above data frame, we have to manipulate this data frame to get the errorbars by using the ‘type’ column having different prices of the bags. To manipulation and perform calculations, we have to use a df.groupby function that has a prototype to check the field and execute the function to evaluate result.

We are using two inbuilt functions of mean and std: 

df.groupby("col_to_group_by").agg([func_1, func_2, func_3, .....])

As we have to evaluate the average price, so apply this groupby on ‘AveragePrice’. Also, check the result of prices and with the visualization display the errorbars

 
 Output:

Errorbar using Mean:

 
 Output:

By the above visualization, it’s clear that organic has a higher mean price than conventional.

Errorbar using Standard Deviation (std):

Output:

Advantages of Errorbars:

• Errorbars are more obstacles.
• They are easy to execute with good estimation values.
• Relatively uniform because of complex interpretation power with a data frame.

Errorbar is the plotted chart that refers to the errors contained in the data frame, which shows the confidence & precision in a set of measurements or calculated values. Error bars help in showing the actual and exact missing parts as well as visually display the errors in different areas in the data frame. Error bars are the descriptive behavior that holds information about the variances in data as well as advice to make proper changes to build data more insightful and impactful for the users.

Here we discuss how we plot errorbar with mean and standard deviation after grouping up the data frame with certain applied conditions such that errors become more truthful to make necessary for obtaining the best results and visualizations. 

Modules Needed:

pip install numpy
pip install pandas
pip install matplotlib

Here is the DataFrame from which we illustrate the errorbars with mean and std: 
 

Output:

groupby the subplots with mean and std to get error bars: 

 
 Output:

Now we see error bars using NumPy keywords of mean and std:

 
 Output:

By the above example, we can see that errors in poor quality are higher than good instead of more good values in the data frame.

Now, we move with another example with data frame below:

 By the above data frame, we have to manipulate this data frame to get the errorbars by using the ‘type’ column having different prices of the bags. To manipulation and perform calculations, we have to use a df.groupby function that has a prototype to check the field and execute the function to evaluate result.

We are using two inbuilt functions of mean and std: 

df.groupby("col_to_group_by").agg([func_1, func_2, func_3, .....])

As we have to evaluate the average price, so apply this groupby on ‘AveragePrice’. Also, check the result of prices and with the visualization display the errorbars

 
 Output:

Errorbar using Mean:

 
 Output:

By the above visualization, it’s clear that organic has a higher mean price than conventional.

Errorbar using Standard Deviation (std):

Output:

Advantages of Errorbars:

• Errorbars are more obstacles.
• They are easy to execute with good estimation values.
• Relatively uniform because of complex interpretation power with a data frame.