Scatter -scatter
Scatter plots showing two sets of data values, the coordinate position of each point is determined by the value of the variable is done by a set of points not connected, two types of variables relevant for observation
. 1 Import numpy AS NP 2 Import matplotlib.pyplot AS PLT # introduced drawing module . 3 . 4 height = [161, 170., 182, 175, 173, 165 ] . 5 weight = [50, 58, 80, 70, 69, 55 ] . 6 . 7 plt.scatter (height, weight) . 8 N = 1000 . 9 X = np.random.randn (N) 10 # Y1 = np.random.randn (N) . 11 Y1 = np.random.randn the -X-+ (N) 0.5 * 12 is 13 is 14 # commands to draw a line scattergram 15 plt.scatter (X, Y1, S = 100, C = ' R & lt ' , = marker 'O ' , alpha = 0.5 ) 16 # S denotes the area of the point, c denotes the color point, marker showing the shape of dots, alpha represents the point of transparency . 17 18 is plt.show ()
line chart
Is a line graph with line segments connecting the respective pattern data consisting used to observe trends in data over time
. 1 Import numpy AS NP 2 Import matplotlib.pyplot AS PLT . 3 Import matplotlib.dates AS mdates . 4 . 5 X np.linspace = (-10, 10, 100 ) . 6 . 7 # Y = X ** 2 . 8 Y = np.sin ( X) . 9 10 plt.plot (X, Y, linestyle = ' -. ' , color = ' G ' , marker = ' ^ ' ) . 11 # line graph basic drawing command, linestyle as to draw the line, color the color line, marker for the spot shape 12 # in matplotlib official website, there are about line, color, shape a comprehensive introduction point 13 14 plt.show()
Bar chart
A variable length of the rectangular charts, for comparing a plurality of data items classified size, typically used for small data sets Analysis
Bar can be single, side by side, stacked fashion drawing
. 1 Import numpy AS NP 2 Import matplotlib.pyplot AS PLT . 3 . 4 # single mode . 5 N =. 5 . 6 Y = [20 is, 10, 30, 25, 15 ] . 7 index = np.arange (N) . 8 . 9 # plt.bar (index = x, Y = height, width = 0.5, Color = 'R & lt') 10 # x represents the number of stripes corresponding to the x-axis, y-axis represents a height corresponding to the height of the bar, 11 # width represents the width of the bars 12 is plt.bar (index, Y, 0.5, Color = ' R & lt ' ) # X =, = height, width =, may be omitted 13 is 14 15 # bar may be put sideways 16 # X needs to assign a value of 0, bottom bar represents the bottom of the block, i.e., corresponding to the coordinates on the vertical axis, . 17 # width bar represents the height of the block placed sideways, corresponding to (lateral) width of the horizontal axis, 18 is # height represents the width of the longitudinal bar block, orientation = 'horizontal' represents a transverse bar to be drawn . 19 PL = plt.bar (X = 0, index = bottom, Color = ' Red ' , Y = width, height = 0.5 , 20 is = Orientation ' Horizontal ' ) 21 is # transverse bar has a second mode, where the y-axis should be the first of several strip fast assignment i.e. index 22 is # PL = plt.barh (y = index, Color = 'Red', Y = width,) 23 is 24 25 plt.show ()
. 1 Import numpy AS NP 2 Import matplotlib.pyplot AS PLT . 3 . 4 # tile painted bar, the two bar graphs a common axis, in parallel with the painting . 5 index = np.arange (. 4 ) . 6 sales_BJ = [52,55,63,53 ] . 7 sales_SH = [44,66,55,41 ] . 8 . 9 bar_width = 0.3 10 plt.bar (index, sales_BJ, bar_width, Color = ' B ' ) . 11 12 is # the parallel bars coordinates on the horizontal axis of the chart can be expressed by index + bar_width 13 # object is not overlapped with the first 14 plt.bar (index + bar_width, sales_SH, bar_width, Color = ' R & lt') 15 16 plt.show()
. 1 Import numpy AS NP 2 Import matplotlib.pyplot AS PLT . 3 . 4 . 5 # laminated drawing . 6 index = np.arange (. 4 ) . 7 sales_BJ = [52,55,63,53 ] . 8 sales_SH = [44,66,55,41 ] . 9 10 bar_width = 0.3 . 11 plt.bar (index, sales_BJ, bar_width, Color = ' B ' ) 12 is 13 is # a second object to be laminated need to add bottom = sales_BJ, showing the object bottom is laminated on a 14 PLT. bar (index, sales_SH, bar_width, Color = ' R & lt ' , bottom = sales_BJ) 15 16 plt.show()