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This article mainly explains "how to use Numpy to analyze the proportion of all kinds of users", interested friends may wish to take a look. The method introduced in this paper is simple, fast and practical. Let's let the editor take you to learn how to use Numpy to analyze the proportion of all kinds of users.

Analysis target

Looking at the last data, some of the data have two user categories: member and non-member.

This time we mainly analyze the proportion of two categories of users in the data.

Data reading and data cleaning

According to the process diagram, we mainly follow the following steps:

The code here is:

# data reading, data cleaning

Def read_clean_data ():

Clndata_arr_list = []

For data_filename in data_filenames:

File = os.path.join (data_path, data_filename)

Data_arr = np.loadtxt (file, skiprows=1, delimiter=',', dtype=bytes) .astype (str)

Cln_arr = np.core.defchararray.replace (data_arr [:,-1],'",'')

Cln_arr = cln_arr.reshape (- 1)

Clndata_arr_list.append (cln_arr)

Year_cln_arr = np.concatenate (clndata_arr_list)

Return year_cln_arr

There are two points to note here:

Because the data is large, we do not have the specific amount of data in the data file, so when using numpy.reshape, we can use numpy.reshape (- 1 numpy 1) so that numpy can replace-1 with the specific value after statistics.

Our need for data is no longer to get the average time, but to take the last column of the data and stack it together using the concatenate method for the next step.

Data analysis

According to the goal of this analysis, we take out the last column of Member type.

In the previous step, we have obtained all the values, in this part, we only need to screen out the values of members and non-members.

We can first take a look at this part of the code after completion:

# data analysis

Def mean_data (year_cln_arr):

Member = year_cln_ ar [year _ cln_arr = = 'Member'] .shape [0]

Casual = year_cln_ ar [year _ cln_arr = = 'Casual'] .shape [0]

Users = [member,casual]

Print (users)

Return users

Again, numpy.shape is used here to get the specific data of the user classification.

Result display

The generated pie chart:

Here is the code to generate the pie chart:

# result display

Plt.figure ()

Plt.pie (users, labels= ['Member',' Casual'], autopct='%.2f%%', shadow=True, explode= (0.05,0))

Plt.axis ('equal')

Plt.tight_layout ()

Plt.savefig (os.path.join (output_path,'. / piechart.png'))

Plt.show ()

At this point, I believe you have a deeper understanding of "how to use Numpy to analyze the proportion of all kinds of users". You might as well do it in practice. Here is the website, more related content can enter the relevant channels to inquire, follow us, continue to learn!

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