The use of Matplotlib visual library

Source: Internet
Author: User
First, Pandas import data &matplotlib basic drawing

# _*_ Coding:utf-8 _*_ Import pandas as PD import Matplotlib.pyplot as PLT # # Author:yz # date:2017-12-3 # ' Pand As import data and DATE format conversion matplotlib Basic drawing: Horizontal ordinate label, caption, coordinate value rotation, etc. ' # import data Unrate = Pd.read_csv ("data/unrate.csv") unrate["DATE" = PD.    To_datetime (unrate["Date"]) # 1948/1/1-> 1948-01-01 # Print (unrate.head) ' ' Date VALUE 0 1948-01-01 3.4 1 1948-02-01 3.8 2 1948-03-01 4.0 3 1948-04-01 3.9 4 1948-05-01 3.5 5 1948-06-01 3.6 6 1948-07-01 3. 6 7 1948-08-01 3.9 8 1948-09-01 3.8 9 1948-10-01 3.7 ' ' # Plt.plot () # plt.show () ' While the Y-axis looks f INE, the x-axis tick labels are too close together and are unreadable We can rotate the x-axis tick labels by degrees s
o they don ' t overlap We can specify degrees of rotation using a float or integer value. ' # first_twelve = unrate[0:12] # plt.plot (first_twelve["DATE"], first_twelve["VALUE"]) # plt.xticks (rotation=45) # x coordinates The value is too long to rotate and then display # # print (Help (Plt.xticks)) # plt.show () '' Xlabel (): Accepts a string value, which gets set as the x-axis label.
Ylabel (): Accepts a string value, which is set as the Y-axis label.
Title (): Accepts a string value, which is set as the plot title.
' First_twelve = Unrate[0:12] Plt.plot (first_twelve[' DATE ', first_twelve[' VALUE ') plt.xticks (rotation=45) Plt.xlabel (' Month ') plt.ylabel (' Unemployment Rate ') plt.title (' monthly unemployment, Trends ') 1948 ()
Second, add the child graph & Specify image size and line color & Add label and specify location

# _*_ Coding:utf-8 _*_ import matplotlib.pyplot as PLT Import pandas as PD import NumPy as NP # Author:yz # date:20 17-12-3 # ' Add Add_subplot (first,second,index) Specifies the image size Plt.figure (figsize= (12, 6)) specifies the color of the line Plt.plot (unrate[0:12][" MONTH "], unrate[0:12][" value "], c=" red ") Add tags and specify location plt.plot (subset[" MONTH "], subset[" value "], C=colors[i], label= Label) plt.legend (loc= ' upper left ') ' "' Add_subplot (First,second,index) A/means number of Row,second means Numbe
R of Column. "# fig = plt.figure () # ax1 = Fig.add_subplot (3, 2, 1) # ax2 = Fig.add_subplot (3, 2, 2) # ax3 = Fig.add_subplot (3, 2, 3 # ax6 = Fig.add_subplot (3, 2, 6) # Ax1.plot (Np.random.randint (1, 5, 5), Np.arange (5)) # Ax2.plot (np.arange) * 3, NP.A Range () # plt.show () ' ' Specify color and size ' unrate = pd.read_csv (' data/unrate.csv ') unrate[' DATE ' = Pd.to_datetime (unrate[" Date "]) unrate[" MONTH "= unrate[" DATE "].dt.month # fig = plt.figure (figsize= (6)) # Figsize Image size # Plt.plot (unrate[ 0:12]["MONTH"], unrate[0: 12]["value"], c= "Red") # C to specify line Color # plt.plot (unrate[12:24]["MONTH"], unrate[12:24]["value"], c= "green") # plt.show () '
    ' lable ' fig = plt.figure (figsize= (6)) colors = [' Red ', ' blue ', ' green ', ' orange ', ' black '] for I in range (5):
    Start_index = i * End_index = (i + 1) * label = STR (1948 + i) subset = Unrate[start_index:end_index]  Plt.plot (subset["MONTH"], subset["VALUE"], C=colors[i], Label=label) # Add lable # Specify the location of lable ' best ' upper Right/left ' ' Lower Right/left ' right ' center Right/left ' upper/lower Center ' center ' plt.legend (loc= ' upper left ') # print (pl t.legend)) Plt.xlabel (' Month, Integer ') plt.ylabel (' Unemployment Rate, Percent ') plt.title (' monthly unemployment Trends, 1948-1952 ') plt.show ()
c. Histogram & Scatter Chart

# _*_ Coding:utf-8 _*_ Import pandas as PD import Matplotlib.pyplot as PLT from numpy import Arange # # Author:yz # Da Te:2017-12-3 # ' Columnar fig, ax = plt.subplots () ax.bar (bar_positions, bar_heights, 0.5) Scatter graph ' ' # # import data # reviews = P D.read_csv ("data/fandango_scores.csv") cols = [' FILM ', ' rt_user_norm ', ' metacritic_user_nom ', ' imdb_norm ', ' Fandango_ ' Ratingvalue ', ' fandango_stars '] norm_reviews = reviews[cols] # print (norm_reviews[:1)) # bar Chart # num_cols = [' Rt_user_n Orm ', ' metacritic_user_nom ', ' imdb_norm ', ' fandango_ratingvalue ', ' Fandango_stars '] # bar_heights = norm_reviews.ix[0, Num_cols].values # Print (bar_heights) # [4.2999999999999998 3.5499999999999998 3.8999999999999999 4.5 5.0] # Bar_positi ONS = Arange (5) + 0.75 # print (bar_positions) # [1 2 3 4 5] # fig, ax = plt.subplots () # Ax.bar (Bar_positions, bar_he Ights, 0.5) # plt.show () # Change the coordinate value to label ax.set_xticklabels (Num_cols, rotation=45) # num_cols = [' Rt_user_norm ', ' Metacrit Ic_user_nom ', ' imdb_norm ', ' FandAngo_ratingvalue ', ' Fandango_stars '] # bar_heights = norm_reviews.ix[0, num_cols].values # Print (bar_heights) # [4.2999    999999999998 3.5499999999999998 3.8999999999999999 4.5 5.0] # bar_positions = Arange (5) + 0.75 # print (bar_positions) # [1 2 3 4 5] # fig, ax = plt.subplots () # Ax.bar (bar_positions, Bar_heights, 0.5) # tick_positions = Range (1,6) # AX.S Et_xticks (tick_positions) # ax.set_xticklabels (Num_cols, rotation=45) # Ax.set_xlabel (' Rating Source ') # ax.set_ Ylabel (' Average Rating ') # ax.set_title (' Average User Rating for Avengers:age of Ultron (2015) ') # plt.show () # fig, ax = Plt.subplots () # ax.hist (norm_reviews[' Fandango_ratingvalue ']) # ax.hist (norm_reviews[' Fandango_ratingvalue '), BINS=20) # ax.hist (norm_reviews[' Fandango_ratingvalue '), Range= (3, 5), bins=20) # plt.show () # Scatter plot # fig, ax = PLT.SUBP Lots () ax.scatter (norm_reviews[' Fandango_ratingvalue '), norm_reviews[' Rt_user_norm ']) # ax.scatter ([4.5, 3], [4.3, 4] ) # (4.5, 4.3) (3, 4) Ax.set_xlabel (' FanDango ') Ax.set_ylabel (' Rotten Tomatoes ') plt.show () 
setting of graphs & coordinates

# _*_ Coding:utf-8 _*_ Import pandas as PD import Matplotlib.pyplot as PLT # # Author:yz # date:2017-12-3 # ' curve "' Women_degrees = pd.read_csv (' data/percent-bachelors-degrees-women-usa.csv ') # Plt.plot (women_degrees[' year '), women_degrees[' Biology '] # plt.show () # Plt.plot (women_degrees[' year '), women_degrees[' Biology '], c= ' Blue ', label= ' Women ') # Plt.plot (women_degrees[' year '), 100-women_degrees[' Biology '], c= ' green ', label= ' Men ') # plt.legend (loc= ' Upper right '] # plt.title (' Percentage of biology Degrees awarded by Gender ') # plt.show () # fig, ax = plt.subplots () # Ax.plot (women_degrees[' year '], women_degrees[' biology '], label= ' Women ') # Ax.plot (women_degrees[' year '), 100-women_ degrees[' Biology '], label= ' Men ') # Ax.tick_params (bottom= "Off", top= "off", left= "off", right= "Off") # Ax.set_title (' Percentage of biology Degrees awarded by Gender ') # ax.legend (loc= "upper Right") # # Plt.show () # fig, ax = plt.subplots () # Ax.plot (women_degrees[' year '), women_degrees[' BioLogy '], c= ' Blue ', label= ' Women ') # Ax.plot (women_degrees[' year '), 100-women_degrees[' Biology '], c= ' green ', label= ' Men ') # Ax.tick_params (bottom= "Off", top= "off", left= "off", right= "Off") # for Key,spine in Ax.spines.items (): # SPI
Ne.set_visible (False) # # End Solution code. # ax.legend (loc= ' upper right ') # plt.show () major_cats = [' Biology ', ' Computer science ', ' Engineering ', ' Math and Statis Tics '] Fig = plt.figure (figsize=) for SP in range (0,4): Ax = Fig.add_subplot (2,2,sp+1) Ax.plot (Women_degr ees[' year '], WOMEN_DEGREES[MAJOR_CATS[SP]], c= ' Blue ', label= ' Women ') ax.plot (women_degrees[' year '), 100-women_

DEGREES[MAJOR_CATS[SP]], c= ' green ', label= ' Men ') # ADD your code here.
# calling Pyplot.legend () here'll add the legend to the last subplot this was created. Plt.legend (loc= ' upper right ") plt.show () major_cats = [' Biology ', ' Computer science ', ' Engineering ', ' Math and Statistics '] Fig = plt.figure (figsize=) for SP in range (0,4): Ax = Fig.Add_subplot (2,2,sp+1) ax.plot (women_degrees[' year ', Women_degrees[major_cats[sp]], c= ' Blue ', label= ' women ') ax.pl OT (women_degrees[' year '], 100-WOMEN_DEGREES[MAJOR_CATS[SP]], c= ' green ', label= ' Men ') for Key,spine in Ax.spines.items
    (): Spine.set_visible (False) Ax.set_xlim (1968) Ax.set_ylim (0,100) ax.set_title (MAJOR_CATS[SP))  Ax.tick_params (bottom= "Off", top= ' off ', left= ' off ', right= ' off ') # calling Pyplot.legend () here'll add the legend to
The last subplot is created. Plt.legend (loc= ' upper right ') plt.show ()
type of line

# _*_ Coding:utf-8 _*_ Import pandas as PD import Matplotlib.pyplot as PLT # author:yz # date:2017-12-3 # Women_d Egrees = pd.read_csv (' data/percent-bachelors-degrees-women-usa.csv ') major_cats = [' Biology ', ' Computer science ', ' 
Engineering ', ' Math and Statistics '] # Cb_dark_blue = (0/255, 107/255, 164/255) # Cb_orange = (255/255, 128/255, 14/255) # fig = Plt.figure (figsize=) # for SP in range (0,4): # ax = Fig.add_subplot (2,2,sp+1) # The color F
or each of the line are assigned here. # Ax.plot (women_degrees[' year ', Women_degrees[major_cats[sp]], c=cb_dark_blue, label= ' Women ') # Ax.plot (women_deg         rees[' year '], 100-WOMEN_DEGREES[MAJOR_CATS[SP]], C=cb_orange, label= ' Men ') # for Key,spine in Ax.spines.items (): #     Spine.set_visible (False) # Ax.set_xlim (1968) # Ax.set_ylim (0,100) # Ax.set_title (MAJOR_CATS[SP)) # Ax.tick_params (bottom= "Off", top= "off", left= "off", right= "Off") # Plt.legend (loc= ' upper right ') # plt.show () #SetTing line Width Cb_dark_blue = (0/255, 107/255, 164/255) Cb_orange = (255/255, 128/255, 14/255) FIG = plt.figure (figsize=

(12, 12)) For SP in range (0,4): Ax = Fig.add_subplot (2,2,sp+1) # Set "line width" specifying how to each line should loo K. Ax.plot (women_degrees[' year ', Women_degrees[major_cats[sp]], c=cb_dark_blue, label= ' women ', linewidth=10) AX.P Lot (women_degrees[' year ', 100-women_degrees[major_cats[sp]], C=cb_orange, label= ' Men ', linewidth=10) for Key,spine I n Ax.spines.items (): spine.set_visible (False) Ax.set_xlim (1968,) Ax.set_ylim (0,100) Ax.set_title ( MAJOR_CATS[SP]) ax.tick_params (bottom= "Off", top= "off", left= "off", right= "Off") plt.legend (loc= ' upper right ') plt.sh OW ()

Contact Us

The content source of this page is from Internet, which doesn't represent Alibaba Cloud's opinion; products and services mentioned on that page don't have any relationship with Alibaba Cloud. If the content of the page makes you feel confusing, please write us an email, we will handle the problem within 5 days after receiving your email.

If you find any instances of plagiarism from the community, please send an email to: info-contact@alibabacloud.com and provide relevant evidence. A staff member will contact you within 5 working days.

A Free Trial That Lets You Build Big!

Start building with 50+ products and up to 12 months usage for Elastic Compute Service

  • Sales Support

    1 on 1 presale consultation

  • After-Sales Support

    24/7 Technical Support 6 Free Tickets per Quarter Faster Response

  • Alibaba Cloud offers highly flexible support services tailored to meet your exact needs.