Shulou(Shulou.com)06/03 Report--

This article is to share with you about the Python visualization of the line chart is how, the editor thinks it is very practical, so share with you to learn, I hope you can get something after reading this article, say nothing, follow the editor to have a look.

Abstract: when using matplotlib to draw a line chart with horizontal axis as date format, there are many skills. This paper introduces the method of drawing daily line chart with the help of stock data returned by Tushare package.

Review:

Analysis of Python data processing (1): date data processing

The data source drawn by the line chart uses the Tushare package to obtain the basic data table of listed companies in the following format:

1import pandas as pd

2data = pd.read ('get_stock_basics.csv',encoding =' utf8')

3print (data.head ())

four

5ts_code symbol name list_status list_date is_hs

6000001.SZ 1 Ping an Bank L 19910403 S

7000002.SZ 2 Vanke A L 19910129 S

8000004.SZ 4 National Agricultural Science and Technology L 19910114 N

9000005.SZ 5th Century Star Source L 19901210 N

Then use resample and to.period methods to summarize the number of listed companies in each year, in the format of Pandas.Series array.

"summarize the number of listed companies in each year

2data = data.set_index (['list_date'])

3data = data.resample ('AS'). Count () [' ts_code']

4data = data.to_period ('A')

5print (data.head ())

6print (data.tail ())

The results are as follows:

8list_date

91990 7

101991 4

111992 37

121993 106

131994 99

14...

15list_date

162014 124

172015 223

182016 227

192017 438

202018 78

1. Series draws line chart directly

First, we can draw a line chart directly using pandas's array Series:

1import matplotlib.pyplot as plt

2plt.style.use ('ggplot') # sets the drawing style

3fig = plt.figure (figsize = (102.6)) # set the size of the border

4ax1 = fig.add_subplot (1pm 1pm 1)

5data.plot () # draw line chart

six

Setting title and horizontal and vertical axis title

8colors1 ='# 6D6D6D' # set the title color to gray

9plt.title ('changes in the number of listed companies in mainland China over the years', color = colors1,fontsize = 18)

10plt.xlabel ('year')

11plt.ylabel ('quantity (home)')

12plt.show ()

It can be found that there are two problems in the figure: one is the lack of numerical labels, and the other is that the Abscissa years are automatically segmented. We want to be able to add a numerical label, and then the axis shows the annual value of each year. Next, you need to use a new method to redraw the line chart.

two。 The line chart perfects the label to create the x ~ y-axis.

2x = np.arange (0Llen (data), 1)

3 ax1.plot (XMagna data.values, # x, y coordinates

4 color ='# C42022 colors, # line chart color is red

5 marker = 'odyne last markersize = 4 # Mark shape and size setting

6)

7ax1.set_xticks (x) # sets the x-axis label to a sequence of natural numbers

8ax1.set_xticklabels (data.index) # change the x-axis label value to year

9plt.xticks (rotation=90) # rotate 90 degrees so that it is not too crowded

ten

11for XMagna y in zip (XMagne data.values):

12 plt.text (x _ center',color y + 10 ~ m% .0f'% y _ my ha = 'colors1,fontsize = 10)

13 #% .0f'y set label format without decimal

1 "sets the title and the horizontal and vertical axis title

15plt.title ('changes in the number of listed companies in mainland China over the years', color = colors1,fontsize = 18)

16plt.xlabel ('year')

17plt.ylabel ('quantity (home)')

Plt.savefig ('stock.png',bbox_inches =' tight',dpi = 300)

19plt.show ()

The improved line chart is as follows:

As you can see, the x-axis data are displayed year by year and the numerical labels are added.

3. Multivariate line chart

The drawing of a unary line chart is introduced above, and when you need to draw a multivariate line chart, the method is also very simple, as long as you repeat the drawing function. Here we take the binary line chart as an example to draw a comparative line chart of the market capitalization changes of two well-known domestic real estate companies Vanke and Poly Real Estate in 2017.

3.1. Data source

The data source still uses the pro.daily_basic () interface of the tushare package, which returns daily stock market data, including the daily market capitalization total_mv. The two stocks we need to get are 000002.SZ (Vanke) and 600048.SH (Poly). Here's the market capitalization data for 2017.

1import tushare as ts

2ts.set_token ('your token') # can be obtained after registration on the official website.

3pro = ts.pro_api ()

4def get_stock ():

5 lst = []

6 ts_codes = ['000002.SZ,' 600048.SH']

7 for ts_code in ts_codes:

8 data = pro.daily_basic (

9 ts_code=ts_code, start_date='20170101', end_date='20180101')

10 print (lst)

11 reutrn lst

12 # the results are as follows: total_mv is the market value of the day (RMB 10,000):

13 # Vanke Real Estate data

14 ts_code trade_date close... Total_mv circ_mv

150 000002.SZ 20171229 31.06... 3.43E+07 3.02E+07

161 000002.SZ 20171228 30.7... 3.39E+07 2.98E+07

172 000002.SZ 20171227 30.79... 3.40E+07 2.99E+07

183 000002.SZ 20171226 30.5... 3.37E+07 2.96E+07

194 000002.SZ 20171225 30.37... 3.35E+07 2.95E+07

twenty

21 # Poly Real Estate data

22 ts_code trade_date close... Total_mv circ_mv

230 600048.SH 20171229 14.15... 1.68E+07 1.66E+07

241 600048.SH 20171228 13.71... 1.63E+07 1.61E+07

252 600048.SH 20171227 13.65... 1.62E+07 1.60E+07

263 600048.SH 20171226 13.85... 1.64E+07 1.63E+07

274 600048.SH 20171225 13.55... 1.61E+07 1.59E+07

The following is to further modify the data, extract the total_mv column from DataFrame, set index to the date, and then use the resample and pd.to_period methods to summarize the market capitalization data on a monthly basis.

1data ['trade_date'] = pd.to_datetime (data [' trade_date'])

Setting index to date

3data = data.set_index (data ['trade_date']) .sort_index (ascending=True)

"summarize and display by month

5data = data.resample ('m')

6data = data.to_period ()

The market value has been changed to 100 million yuan.

8market_value = data ['total_mv'] / 10000

nine

1the results of the two are as follows: Vanke Real Estate:

112017-01 2291.973270

122017-02 2286.331037

132017-03 2306.894790

142017-04 2266.337906

152017-05 2131.053098

162017-06 2457.716659

172017-07 2686.982164

182017-08 2524.462077

192017-09 2904.085487

202017-10 2976.999550

212017-11 3263.374043

222017-12 3317.107474

2. Poly Real Estate:

242017-01 1089.008286

252017-02 1120.023350

262017-03 1145.731640

272017-04 1153.760435

282017-05 1108.230609

292017-06 1157.276044

302017-07 1244.966905

312017-08 1203.580209

322017-09 1290.706606

332017-10 1244.438756

342017-11 1336.661916

352017-12 1531.150616

3.2. Draw a binary line chart

Using the Series data above, you can make a map.

Set the drawing style

2plt.style.use ('ggplot')

3fig = plt.figure (figsize = (102.6))

4colors1 ='# 6D6D6D' # title color

five

Data1 Vanke, data2 Poly

7data1 = lst [0]

8data2 = lst [1]

Draw the first line chart

10data1.plot (

11color ='# C42022 colors, # line chart color

12marker = 'odyne last markersize = 4, # Mark shape and size settings

13label = 'Vanke'

14)

Draw the second line chart

16data2.plot (

17color ='# 4191C0colors, # line chart color

18marker = 'odyne last markersize = 4, # Mark shape and size settings

19label = 'Poly'

20)

More bars can be drawn in 2 posts.

2. Set the title and the horizontal and vertical axis title

23plt.title ('comparison of market capitalization between Vanke and Poly Real Estate in 2017', color = colors1,fontsize = 18)

24plt.xlabel ('month')

25plt.ylabel ('market capitalization (RMB 100 million)')

26plt.savefig ('stock1.png',bbox_inches =' tight',dpi = 300)

27plt.legend () # display legend

28plt.show ()

The drawing results are as follows:

If you want to add a numeric label, you can use the following code:

Draw the first line chart

Coach creates a label on the x ~ (nd) y axis

3x = np.arange (0Llen (data1), 1)

4ax1.plot (XMagna data1.values, # x, y coordinates

5color ='# C42022 colors, # line chart color red

6marker = 'odyne last markersize = 4, # Mark shape and size settings

7label = 'Vanke'

8)

9ax1.set_xticks (x) # set x-axis label

10ax1.set_xticklabels (data1.index) # sets the x-axis label value

1cm plt.xticks (rotation=90)

12for XMagna y in zip (XMagol data1.values):

13 plt.text (x _ center',color y + 10 ~ m% .0f'% y ~ ha = 'colors1,fontsize = 10)

14 #% .0f'y set label format without decimal

fifteen

Draw the second line chart

17x = np.arange (0Llen (data2), 1)

eighteen

19ax1.plot (XMagna data2.values, # x, y coordinates

20color ='# 4191C0cm, # line chart color blue

21marker = 'odyne last markersize = 4, # Mark shape and size settings

22label = 'Poly'

23)

24ax1.set_xticks (x) # set x-axis label

25ax1.set_xticklabels (data2.index) # sets the x-axis label value

2 plt.xticks (rotation=90)

27for XMagna y in zip (XMeno data2.values):

28 plt.text (x _ center',color y + 10 ~ m% .0f'% y _ my ha = 'colors1,fontsize = 10)

29 #% .0f'y set label format without decimal

thirty

3 setting title and horizontal and vertical axis title

32plt.title ('comparison of market capitalization between Vanke and Poly Real Estate in 2017', color = colors1,fontsize = 18)

33plt.xlabel ('month')

34plt.ylabel ('market capitalization (RMB 100 million)')

thirty-five

36plt.savefig ('stock1.png',bbox_inches =' tight',dpi = 300)

37plt.legend () # display legend

38plt.show ()

The result is shown in the following figure:

It can be seen that the market capitalization of the two stocks has been rising since the beginning of 2017, and Vanke's market capitalization is about twice that of Poly.

The above is what the line chart of Python visualization looks like, and the editor believes that there are some knowledge points that we may see or use in our daily work. I hope you can learn more from this article. For more details, please follow the industry information channel.

Welcome to subscribe "Shulou Technology Information " to get latest news, interesting things and hot topics in the IT industry, and controls the hottest and latest Internet news, technology news and IT industry trends.