使用matplotlib包绘制折线图
image.png
# 导入所需的python包
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
# 设置绘图格式
plt.style.use('seaborn')
%matplotlib inline
# 创建示例数据
values=np.cumsum(np.random.randn(1000,1))
# 查看示例数据
values[1:10]
array([ 0.24297415, 0.8161543 , 0.32656029, -0.17953043, -0.80225196,
-1.27380905, -0.28938979, -3.42407114, -3.58398279])</pre>
绘制基础折线图
# use the plot function
plt.plot(values)
# show the graph
plt.show()
image.png
# 绘制未排序数据的折线图
# import the iris dataset
# 加载示例数据
df = sns.load_dataset('iris')
df.head(10)
|
sepal_length |
sepal_width |
petal_length |
petal_width |
species |
| 0 |
5.1 |
3.5 |
1.4 |
0.2 |
setosa |
| 1 |
4.9 |
3.0 |
1.4 |
0.2 |
setosa |
| 2 |
4.7 |
3.2 |
1.3 |
0.2 |
setosa |
| 3 |
4.6 |
3.1 |
1.5 |
0.2 |
setosa |
| 4 |
5.0 |
3.6 |
1.4 |
0.2 |
setosa |
| 5 |
5.4 |
3.9 |
1.7 |
0.4 |
setosa |
| 6 |
4.6 |
3.4 |
1.4 |
0.3 |
setosa |
| 7 |
5.0 |
3.4 |
1.5 |
0.2 |
setosa |
| 8 |
4.4 |
2.9 |
1.4 |
0.2 |
setosa |
| 9 |
4.9 |
3.1 |
1.5 |
0.1 |
setosa |
# plot
plt.plot( 'sepal_width', 'sepal_length', data=df)
# show the graph
plt.show()
image.png
# 绘制排序后数据的折线图
# 构建示例数据
df=pd.DataFrame({'xvalues': range(1,101), 'yvalues': np.random.randn(100) })
df.head(10)
|
xvalues |
yvalues |
| 0 |
1 |
0.876885 |
| 1 |
2 |
0.695569 |
| 2 |
3 |
0.807841 |
| 3 |
4 |
0.447100 |
| 4 |
5 |
-0.186339 |
| 5 |
6 |
-1.212736 |
| 6 |
7 |
0.235604 |
| 7 |
8 |
1.157926 |
| 8 |
9 |
-0.733519 |
| 9 |
10 |
0.864461 |
# plot
plt.plot( 'xvalues', 'yvalues', data=df)
# show the graph
plt.show()
image.png
自定义线的颜色
# 构建示例数据
df=pd.DataFrame({'x_values': range(1,11), 'y_values': np.random.randn(10) })
# Draw plot
# 设置color参数自定义线的颜色
plt.plot( 'x_values', 'y_values', data=df, color='skyblue')
plt.show()
image.png
# Draw line chart by modifiying transparency of the line
# 设置alpha参数更改线的透明度
plt.plot( 'x_values', 'y_values', data=df, color='red', alpha=0.3)
# Show plot
plt.show()
image.png
自定义线的类型
# Draw line chart with dashed line
# 设置linestyle参数自定义闲的类型
plt.plot( 'x_values', 'y_values', data=df, linestyle='dashed')
# Show graph
plt.show()
image.png
# 查看不同的线型
plt.plot( [1,1.1,1,1.1,1], linestyle='-' , linewidth=4)
plt.text(1.5, 1.3, "linestyle = '-' ", horizontalalignment='left', size='medium', color='C0', weight='semibold')
plt.plot( [2,2.1,2,2.1,2], linestyle='--' , linewidth=4 )
plt.text(1.5, 2.3, "linestyle = '--' ", horizontalalignment='left', size='medium', color='C1', weight='semibold')
plt.plot( [3,3.1,3,3.1,3], linestyle='-.' , linewidth=4 )
plt.text(1.5, 3.3, "linestyle = '-.' ", horizontalalignment='left', size='medium', color='C2', weight='semibold')
plt.plot( [4,4.1,4,4.1,4], linestyle=':' , linewidth=4 )
plt.text(1.5, 4.3, "linestyle = ':' ", horizontalalignment='left', size='medium', color='C3', weight='semibold')
plt.axis('off')
plt.show()
image.png
自定义线的宽度
# Modify line width of the graph
# 设置linewidth参数自定义线的宽度
plt.plot( 'x_values', 'y_values', data=df, linewidth=22)
# Show graph
plt.show()
image.png
绘制多线折线图
# Data
# 构建示例数据
df=pd.DataFrame({'x_values': range(1,11), 'y1_values': np.random.randn(10), 'y2_values': np.random.randn(10)+range(1,11), 'y3_values': np.random.randn(10)+range(11,21) })
df.head(10)
|
x_values |
y1_values |
y2_values |
y3_values |
| 0 |
1 |
1.067931 |
1.387085 |
10.330824 |
| 1 |
2 |
-0.539553 |
1.718083 |
12.094820 |
| 2 |
3 |
0.031352 |
2.526630 |
11.560346 |
| 3 |
4 |
-0.693288 |
3.364125 |
12.002817 |
| 4 |
5 |
-0.176465 |
6.821868 |
13.932456 |
| 5 |
6 |
1.109269 |
5.875778 |
15.407165 |
| 6 |
7 |
-0.750049 |
5.325365 |
18.765016 |
| 7 |
8 |
2.084154 |
8.578474 |
18.401151 |
| 8 |
9 |
0.418775 |
9.524832 |
19.062925 |
| 9 |
10 |
2.051114 |
9.682992 |
18.867805 |
# multiple line plots
plt.plot( 'x_values', 'y1_values', data=df, marker='o', markerfacecolor='blue', markersize=12, color='skyblue', linewidth=4)
plt.plot( 'x_values', 'y2_values', data=df, marker='', color='olive', linewidth=2)
plt.plot( 'x_values', 'y3_values', data=df, marker='', color='red', linewidth=3, linestyle='dashed', label="toto")
# show legend
plt.legend()
# show graph
plt.show()
image.png
高亮特定折线
# Change the style of plot
plt.style.use('seaborn-darkgrid')
# set figure size
my_dpi=96
plt.figure(figsize=(480/my_dpi, 480/my_dpi), dpi=my_dpi)
# Make a data frame
df=pd.DataFrame({'x': range(1,11), 'y1': np.random.randn(10), 'y2': np.random.randn(10)+range(1,11), 'y3': np.random.randn(10)+range(11,21), 'y4': np.random.randn(10)+range(6,16), 'y5': np.random.randn(10)+range(4,14)+(0,0,0,0,0,0,0,-3,-8,-6), 'y6': np.random.randn(10)+range(2,12), 'y7': np.random.randn(10)+range(5,15), 'y8': np.random.randn(10)+range(4,14) })
df.head(10)
|
x |
y1 |
y2 |
y3 |
y4 |
y5 |
y6 |
y7 |
y8 |
| 0 |
1 |
-0.687924 |
2.584799 |
11.480629 |
7.847702 |
3.962812 |
2.817641 |
4.716701 |
5.037785 |
| 1 |
2 |
-1.190644 |
2.509128 |
10.639934 |
6.096679 |
6.023990 |
3.593341 |
5.784641 |
3.675451 |
| 2 |
3 |
-0.355472 |
1.879478 |
13.651546 |
8.620174 |
6.008777 |
2.712259 |
6.934400 |
6.219872 |
| 3 |
4 |
0.978479 |
4.565185 |
12.986859 |
9.257281 |
7.801419 |
4.372574 |
8.886010 |
7.617974 |
| 4 |
5 |
-0.137379 |
6.454554 |
13.882441 |
8.835127 |
6.986779 |
7.970914 |
8.343824 |
9.527383 |
| 5 |
6 |
1.479008 |
4.857621 |
16.752558 |
11.290006 |
8.520126 |
6.507897 |
10.274646 |
7.339675 |
| 6 |
7 |
-0.862117 |
4.919770 |
18.905680 |
13.265195 |
10.892788 |
7.208971 |
10.757919 |
8.773337 |
| 7 |
8 |
-0.787397 |
9.109201 |
17.460583 |
12.353985 |
7.479977 |
8.964906 |
12.300782 |
11.636784 |
| 8 |
9 |
0.815745 |
8.755666 |
18.685683 |
12.728094 |
4.306399 |
10.714981 |
13.400910 |
11.165933 |
| 9 |
10 |
0.443319 |
9.686925 |
20.563202 |
16.168934 |
6.827163 |
10.188284 |
15.023878 |
13.611045 |
# 绘制多条折线图
# plot multiple lines
for column in df.drop('x', axis=1):
plt.plot(df['x'], df[column], marker='', color='grey', linewidth=1, alpha=0.4)
# 高亮其中的一条折线
# Now re do the interesting curve, but biger with distinct color
plt.plot(df['x'], df['y5'], marker='', color='orange', linewidth=4, alpha=0.7)
# Change x axis limit
plt.xlim(0,12)
# 给每条折线添加注释信息
# Let's annotate the plot
num=0
for i in df.values[9][1:]:
num+=1
name=list(df)[num]
if name != 'y5':
plt.text(10.2, i, name, horizontalalignment='left', size='small', color='gray')
# And add a special annotation for the group we are interested in
plt.text(10.2, df.y5.tail(1), 'Mr Orange', horizontalalignment='left', size='small', color='orange')
# 添加标题和坐标轴
# Add titles
plt.title("Evolution of Mr Orange vs other students", loc='left', fontsize=12, fontweight=0, color='orange')
plt.xlabel("Time")
plt.ylabel("Score")
# Show the graph
plt.show()
image.png
绘制分面折线图
# Initialize the figure style
plt.style.use('seaborn-darkgrid')
# create a color palette
palette = plt.get_cmap('Set1')
# 构建示例数据
# Make a data frame
df=pd.DataFrame({'x': range(1,11), 'y1': np.random.randn(10), 'y2': np.random.randn(10)+range(1,11), 'y3': np.random.randn(10)+range(11,21), 'y4': np.random.randn(10)+range(6,16), 'y5': np.random.randn(10)+range(4,14)+(0,0,0,0,0,0,0,-3,-8,-6), 'y6': np.random.randn(10)+range(2,12), 'y7': np.random.randn(10)+range(5,15), 'y8': np.random.randn(10)+range(4,14), 'y9': np.random.randn(10)+range(4,14) })
df.head(10)
|
x |
y1 |
y2 |
y3 |
y4 |
y5 |
y6 |
y7 |
y8 |
y9 |
| 0 |
1 |
0.705547 |
-0.185696 |
13.079950 |
4.935681 |
3.558236 |
1.987303 |
3.777348 |
4.319105 |
3.013749 |
| 1 |
2 |
1.207474 |
3.093301 |
12.163533 |
5.769417 |
5.644947 |
3.789781 |
7.864374 |
4.913551 |
5.306410 |
| 2 |
3 |
-1.078666 |
3.198830 |
12.653673 |
8.683952 |
7.618591 |
3.482401 |
7.515903 |
5.254345 |
5.381230 |
| 3 |
4 |
-1.171316 |
4.410335 |
12.971149 |
10.613208 |
6.895600 |
4.867909 |
8.421857 |
7.340826 |
7.443260 |
| 4 |
5 |
1.270850 |
5.748285 |
16.414187 |
9.581892 |
7.723042 |
6.661644 |
8.781812 |
8.272855 |
7.745660 |
| 5 |
6 |
-0.302443 |
6.361198 |
16.327518 |
9.788331 |
8.850974 |
6.881167 |
9.072248 |
10.308536 |
7.794249 |
| 6 |
7 |
-0.475574 |
7.620870 |
17.139569 |
13.205853 |
11.437107 |
6.388262 |
11.043212 |
9.883755 |
10.771894 |
| 7 |
8 |
0.363418 |
6.782794 |
17.710851 |
13.262601 |
9.111311 |
9.904678 |
13.348669 |
10.125153 |
10.947822 |
| 8 |
9 |
3.034346 |
10.164225 |
19.036592 |
14.160345 |
4.305579 |
8.897607 |
12.429754 |
11.348469 |
11.271929 |
| 9 |
10 |
1.141996 |
9.590279 |
19.169931 |
16.149196 |
6.473655 |
10.561281 |
14.805126 |
12.603702 |
11.674404 |
# multiple line plot
num=0
for column in df.drop('x', axis=1):
num+=1
# Find the right spot on the plot
plt.subplot(3,3, num)
# Plot the lineplot
plt.plot(df['x'], df[column], marker='', color=palette(num), linewidth=1.9, alpha=0.9, label=column)
# Same limits for every chart
plt.xlim(0,10)
plt.ylim(-2,22)
# Not ticks everywhere
if num in range(7) :
plt.tick_params(labelbottom='off')
if num not in [1,4,7] :
plt.tick_params(labelleft='off')
# Add title
plt.title(column, loc='center', fontsize=12, fontweight=0, color=palette(num) )
# general title
plt.suptitle("How the 9 students improved\nthese past few days?", fontsize=13, fontweight=0, color='black', style='italic', y=1.02)
# Axis titles
plt.text(5, 0, 'Time', ha='center', va='center')
plt.text(0, 10, 'Note', ha='center', va='center', rotation='vertical')
# Show the graph
plt.show()
image.png
# multiple line plot
num=0
for column in df.drop('x', axis=1):
num+=1
# Find the right spot on the plot
plt.subplot(3,3, num)
# plot every group, but discrete
for v in df.drop('x', axis=1):
plt.plot(df['x'], df[v], marker='', color='grey', linewidth=1, alpha=0.3)
# Plot the lineplot
plt.plot(df['x'], df[column], marker='', color=palette(num), linewidth=3, alpha=0.9, label=column)
# Same limits for every chart
plt.xlim(0,10)
plt.ylim(-2,22)
# Not ticks everywhere
if num in range(7) :
plt.tick_params(labelbottom='off')
if num not in [1,4,7] :
plt.tick_params(labelleft='off')
# Add title
plt.title(column, loc='center', fontsize=12, fontweight=0, color=palette(num) )
# general title
plt.suptitle("How the 9 students improved\nthese past few days?", fontsize=13, fontweight=0, color='black', style='italic', y=1.02)
# Axis titles
plt.text(5, 0, 'Time', ha='center', va='center')
plt.text(0, 10, 'Note', ha='center', va='center', rotation='vertical')
# Show the graph
plt.show()
image.png
