# Font settings
plt.rcParams['font.sans-serif'] = ["SimHei"]
plt.rcParams["axes.unicode_minus"] = False
Matplotlib: It's a Python Of 2D Drawing library , adopt Matplotlib, Developers can only need a few lines of code , You can generate a line chart , Histogram , Bar chart , The pie chart , Scatter plot, etc. .plot Is a drawing function , His parameters :plot([x],y,[fmt],data=None,**kwargs)
（1） Dotted line form
（2） line color
import matplotlib.pyplot as plt
import numpy as np
# Original line diagram
plt.plot(range(10),[np.random.randint(0,10) for x in range(10)])
# Point line diagram
plt.plot(range(10),[np.random.randint(0,10) for x in range(10)],"*")
# line color
plt.plot([1,2,3,4,5],[1,2,3,4,5],'r') # Set the color line to red
1、 Set chart title :plt.title
2、 Set axis title :plt.xlabel & plt.ylabel - Title Title
3、 Set the axis scale :plt.xticks & plt.yticks - The length of the scale , Scale title
x = range(10)
y = [np.random.randint(0,10) for x in range(10)]
plt.plot(x,y,linewidth=10,color='red')
# Set chart title
plt.title("sin function ")
# Set axis title
plt.xlabel("x Axis ")
plt.ylabel("y Axis ")
# Set the axis scale
plt.xticks(range(10),[" The first %d God "%x for x in range(1,10)])
plt.yticks(range(10),[" The first %d God "%x for x in range(1,10)])
# Load Fonts
plt.rcParams['font.sans-serif'] = ["SimHei"]
plt.rcParams["axes.unicode_minus"] = False
marker: Key point accent mark
x = range(10)
y = [np.random.randint(0,10) for x in range(10)]
plt.plot(x,y,linewidth=10,color='red')
# Key marks
plt.plot(x,y,marker="o",markerfacecolor='k',markersize=10)
annotate: Annotated text
x = range(10)
y = [np.random.randint(0,10) for x in range(10)]
plt.plot(x,y,linewidth=10,color='red')
# Key marks
plt.plot(x,y,marker="o",markerfacecolor='k',markersize=10)
# Note text settings
plt.annotate('local max', xy=(5, 5), xytext=(10,15),
arrowprops=dict(facecolor='black',shrink=0.05),
)
plt.figure: Adjust the size and pixels of the picture
`num`: The number of the drawing ,
`figsize`: It's in inches ,
`dpi`: Pixels per inch ,
`facecolor`: Picture background color ,
`edgecolor`: Border color ,
`frameon`: Whether to draw drawing board .
x = range(10)
y = [np.random.randint(0,10) for x in range(10)]
# Set up graphic styles
plt.figure(figsize=(20,10),dpi=80)
plt.plot(x,y,linewidth=10,color='red')
1. Quantity statistics .
2. frequency count .
barh: Bar chart
1. `x`: An array or list , Representing the bar graph that needs to be drawn x The coordinate point of the axis .
2. `height`: An array or list , Represents the bar graph that needs to be drawn y The coordinate point of the axis .
3. `width`: The width of each bar , The default is 0.8 Width .
4. `bottom`:`y` The baseline of the axis , The default is 0, That is, the distance from the bottom is 0.
5. `align`: Alignment mode , The default is `center`, That is to say, with the designated `x` The coordinates are centered , And for `edge`, Edge alignment , Right or left , see `width` Plus or minus .
6. `color`: The color of the bar graph .
movies = {
" Wandering the earth ":40.78,
" Fast life ":15.77,
" Crazy aliens ":20.83,
" The king of new comedy ":6.10,
" Integrity ":1.10,
" Detective Pu Songling ":1.49,
" Peppa pig has a big year ":1.22,
" Bear haunt · primeval ages ":6.71
}
plt.barh(np.arange(len(movies)),list(movies.values()))
plt.yticks(np.arange(len(movies)),list(movies.keys()),fontproperties=font)
plt.grid()
2.2、 Grouped bars
movies = {
" Wandering the earth ":[2.01,4.59,7.99,11.83,16],
" Fast life ":[3.19,5.08,6.73,8.10,9.35],
" Crazy aliens ":[4.07,6.92,9.30,11.29,13.03],
" The king of new comedy ":[2.72,3.79,4.45,4.83,5.11],
" Integrity ":[0.56,0.74,0.83,0.88,0.92],
" Detective Pu Songling ":[0.66,0.95,1.10,1.17,1.23],
" Peppa pig has a big year ":[0.58,0.81,0.94,1.01,1.07],
" Bear haunt · primeval ages ":[1.13,1.96,2.73,3.42,4.05]
}
plt.figure(figsize=(20,8))
width = 0.75
bin_width = width/5
movie_pd = pd.DataFrame(movies)
ind = np.arange(0,len(movies))
# First option
for index in movie_pd.index:
day_tickets = movie_pd.iloc[index]
xs = ind-(bin_width*(2-index))
plt.bar(xs,day_tickets,width=bin_width,label=" The first %d God "%(index+1))
for ticket,x in zip(day_tickets,xs):
plt.annotate(ticket,xy=(x,ticket),xytext=(x-0.1,ticket+0.1))
# Set legend
plt.ylabel(" Company : Billion ")
plt.title(" Before the Spring Festival 5 Days movie box office record ")
# Set up x Axis coordinates
plt.xticks(ind,movie_pd.columns)
plt.xlim
plt.grid(True)
plt.show()
menMeans = (20, 35, 30, 35, 27)
womenMeans = (25, 32, 34, 20, 25)
groupNames = ('G1','G2','G3','G4','G5')
xs = np.arange(len(menMeans))
plt.bar(xs,menMeans)
plt.bar(xs,womenMeans,bottom=menMeans)
plt.xticks(xs,groupNames)
plt.show()
plt.hist: Histogram
1. x: Arrays or sequences that can loop ;
2. bins: Numbers or sequences （ Array / List etc. ）;
3. range: Tuples or None, If it's a tuple , The specified `x` Divide the maximum and minimum of the interval ;
4. density: The default is `False`, If it is equal to `True`, Then we'll use the frequency distribution histogram ;
5. cumulative: If this and `density` All equal to `True`, Then the first parameter of the return value will continue to accumulate , In the end, it's equal to `1`.
1. Display the distribution of data quantity in each group .
2. Used to observe abnormal or isolated data .
3. The number of samples taken is too small , There will be a big error , Low credibility , It's not statistically significant . therefore , The number of samples should not be less than 50 individual .
durations = [131, 98, 125, 131, 124, 139, 131, 117, 128, 108, 135, 138, 131, 102, 107, 114, 119, 128, 121, 142, 127, 130, 124, 101, 110, 116, 117, 110, 128, 128, 115, 99, 136, 126, 134, 95, 138, 117, 111,78, 132, 124, 113, 150, 110, 117, 86, 95, 144, 105, 126, 130,126, 130, 126, 116, 123, 106, 112, 138, 123, 86, 101, 99, 136,123, 117, 119, 105, 137, 123, 128, 125, 104, 109, 134, 125, 127,105, 120, 107, 129, 116, 108, 132, 103, 136, 118, 102, 120, 114,105, 115, 132, 145, 119, 121, 112, 139, 125, 138, 109, 132, 134,156, 106, 117, 127, 144, 139, 139, 119, 140, 83, 110, 102,123,107, 143, 115, 136, 118, 139, 123, 112, 118, 125, 109, 119, 133,112, 114, 122, 109, 106, 123, 116, 131, 127, 115, 118, 112, 135,115, 146, 137, 116, 103, 144, 83, 123, 111, 110, 111, 100, 154,136, 100, 118, 119, 133, 134, 106, 129, 126, 110, 111, 109, 141,120, 117, 106, 149, 122, 122, 110, 118, 127, 121, 114, 125, 126,114, 140, 103, 130, 141, 117, 106, 114, 121, 114, 133, 137, 92,121, 112, 146, 97, 137, 105, 98, 117, 112, 81, 97, 139, 113,134, 106, 144, 110, 137, 137, 111, 104, 117, 100, 111, 101, 110,105, 129, 137, 112, 120, 113, 133, 112, 83, 94, 146, 133, 101,131, 116, 111, 84, 137, 115, 122, 106, 144, 109, 123, 116, 111,111, 133, 150]
plt.figure(figsize=(15,5))
nums,bins,patches = plt.hist(durations,bins=20,edgecolor='k')
plt.xticks(bins,bins)
for num,bin in zip(nums,bins):
plt.annotate(num,xy=(bin,num),xytext=(bin+1.5,num+0.5))
plt.show()
density: Frequency squared distribution diagram
nums,bins,patches = plt.hist(durations,bins=20,edgecolor='k',density=True,cumulative=True)
plt.xticks(bins,bins)
for num,bin in zip(nums,bins):
plt.annotate("%.4f"%num,xy=(bin,num),xytext=(bin+0.2,num+0.0005))
cumulative Parameters :nums The sum of is 1
plt.figure(figsize=(15,5))
nums,bins,patches = plt.hist(durations,bins=20,edgecolor='k',density=True,cumulative=True)
plt.xticks(bins,bins)
for num,bin in zip(nums,bins):
plt.annotate("%.4f"%num,xy=(bin,num),xytext=(bin+0.2,num+0.0005))
plt.scatter: Scatter plot ：
1. x,y: Namely x Axis and y Axis data set . The data length of the two must be the same .
2. s: The size of the point .
3. c: The color of the point .
4. marker: Marker points , The default is dot , You can also change it to something else .
plt.scatter(x =data_month_sum["sumprice"] # Pass in X Variable data
,y=data_month_sum["Quantity"] # Pass in Y Variable data
,marker='*' # The shape of the dot
,s=10 # Size of points
,c='r' # The color of the point
)
plt.show()
The pie chart : A circular statistical chart divided into several sectors , Used to describe quantities 、 The relative relationship between frequencies or percentages .
stay matplotlib in , Can pass plt.pie To achieve , The parameters are as follows ：
x： The scale sequence of the pie chart .labels： The name text of each block on the pie chart .explode： Set whether or not to separate the pie chart for certain blocks .autopct： Set the display mode of proportional text . For example, keep a few decimals and so on .shadow： Show shadows .textprops： Properties of text （ Color , Size, etc ）.plt.figure(figsize=(8,8),dpi=100,facecolor='white')
plt.pie(x = StockCode.values, # Data in
radius=1.5, # radius
autopct='%.2f%%' # Percentage display
,pctdistance=0.6, # Percentage distance to center ratio
labels=StockCode.index, # label
labeldistance=1.1, # Scale of label distance from center of circle
wedgeprops ={
'linewidth':1.5,'edgecolor':'green'}, # The width and color of the border
textprops={
'fontsize':10,'color':'blue'}) # Text font size and color
plt.title(' Proportion of commodity sales ',pad=100) # Set the title and the position from the coordinate axis
plt.show()
The drawing method of the box diagram is :
:1、 First, find the upper limit of a set of data 、 Lower limit 、 Median （Q2） And the next quartile （Q1） And the upper quartile （Q3）
:2、 Then connect the two quartiles to draw the box
:3、 Then connect the maximum and minimum values to the box , The median is in the middle of the box .
Median : Sort the data from small to large , Then the middle value is the median , If the number of data is even , Then the average of the two numbers in the middle is the median .
The upper and lower quartiles : Also put the data in order , Divide the data into 4 Share . Appear in the `25%` The position is called the lower quartile , Appear in the `75%` The number in position is called the upper quartile . But the method of determining the position of the quartile is not fixed , There are several algorithms , The results of each method will be different , But the difference won't be big .
The calculation rule of the upper and lower limits is ：
IQR=Q3-Q1
ceiling =Q3+1.5IQR
Lower limit =Q1-1.5IQR
stay matplotlib There is plt.boxplot To draw a box diagram , The parameters of this method are as follows ：
x： The data of the box diagram to be drawn .notch： Whether to show the confidence interval , The default is False. If set to True, Then there will be a gap in the box .sym： The symbol representing the outlier indicates , The default is dot .vert： Whether it is vertical , The default is True, If set to False Then show it horizontally .whis： The coefficients of the upper and lower limits , The default is 1.5, That is, the upper limit is Q3+1.5IQR, You can change it to something else . It can also be a sequence , If it's a sequence , Then the two values in the sequence represent the lower limit and the upper limit respectively , Instead of having to pass IQR To calculate .positions： Set the position of each box .widths： Set the width of each box .labels： Every box label.meanline and showmeans： If both are True, Then the line of the average will be drawn .# boxplot - Mainly observe whether the data are abnormal （ outliers ）
# Box whisker -75% and 25% Quantile of +/-1.5 Multiple and differential potential difference
plt.figure(figsize=(6.4,4.8),dpi=100)
# Whether to fill the box color , Whether to display the mean value , Whether to display outliers , Box setting , Outliers setting , Mean setting , Median setting
plt.boxplot(x=UnitPrice # Incoming data
,patch_artist=True # Whether to fill the box color
,showmeans=True # Whether to display the mean value
,showfliers=True # Whether to display outliers
,boxprops={
'color':'black','facecolor':'white'} # Box setting
,flierprops={
'marker':'o','markersize':4,'markerfacecolor':'red'} # Outliers setting
,meanprops={
'marker':'o','markersize':6,'markerfacecolor':'indianred'} # Mean setting
,medianprops={
'linestyle':'--','color':'blue'} # Median setting
)
plt.show()
Radar map : It's also called spider web chart , It can be used to show the strength of variables with three or more dimensions
plt.polar To map the radar ,x The coordinates of the axis should be in radians （2*PI=360°）
import numpy as np
properties = [' Output ','KDA',' development ',' Regiment war ',' Existence ']
values = [40,91,44,90,95,40]
theta = np.linspace(0,np.pi*2,6)
plt.polar(theta,values)
plt.xticks(theta,properties)
plt.fill(theta,values)
polar It doesn't complete the closed drawing of lines , So we need to draw in theta neutralization values At the end, repeat the addition of the second 0 Value of position , And then when you draw it, you can work with the second 1 The two points are closed .polar Just drawing lines , So if you want to color fill the inside , So you need to call fill Function to implement .polar The default circle coordinates are angles , If we want to change it to text , So you can go through xticks To set up .
A treasure cartoon avatar 50 yuan? 1 line of Python code, dont pay IQ tax again
Hello everyone , I am a Python
