使用matplotlib包绘制棒棒糖图
# libraries
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
# create data
x=range(1,41)
values=np.random.uniform(size=40)
# stem function
plt.stem(x, values)
plt.ylim(0, 1.2)
plt.show()
image.png
# stem function: If x is not provided, a sequence of numbers is created by python:
plt.stem(values)
plt.show()
image.png
# Create a dataframe
df = pd.DataFrame({'group':list(map(chr, range(65, 85))), 'values':np.random.uniform(size=20) })
# Reorder it based on the values:
ordered_df = df.sort_values(by='values')
my_range=range(1,len(df.index)+1)
ordered_df.head()
| group | values | |
|---|---|---|
| 17 | R | 0.081097 |
| 8 | I | 0.145329 |
| 15 | P | 0.215493 |
| 6 | G | 0.241037 |
| 1 | B | 0.249132 |
# Make the plot
plt.stem(ordered_df['values'])
plt.xticks( my_range, ordered_df['group'])
plt.show()
image.png
# Horizontal version
plt.hlines(y=my_range, xmin=0, xmax=ordered_df['values'], color='skyblue')
plt.plot(ordered_df['values'], my_range, "D")
plt.yticks(my_range, ordered_df['group'])
plt.show()
image.png
# change color and shape and size and edges
(markers, stemlines, baseline) = plt.stem(values)
plt.setp(markers, marker='D', markersize=10, markeredgecolor="orange", markeredgewidth=2)
plt.show()
image.png
# custom the stem lines
(markers, stemlines, baseline) = plt.stem(values)
plt.setp(stemlines, linestyle="-", color="olive", linewidth=0.5 )
plt.show()
image.png
# Create a dataframe
value1=np.random.uniform(size=20)
value2=value1+np.random.uniform(size=20)/4
df = pd.DataFrame({'group':list(map(chr, range(65, 85))), 'value1':value1 , 'value2':value2 })
# Reorder it following the values of the first value:
ordered_df = df.sort_values(by='value1')
my_range=range(1,len(df.index)+1)
# The horizontal plot is made using the hline function
plt.hlines(y=my_range, xmin=ordered_df['value1'], xmax=ordered_df['value2'], color='grey', alpha=0.4)
plt.scatter(ordered_df['value1'], my_range, color='skyblue', alpha=1, label='value1')
plt.scatter(ordered_df['value2'], my_range, color='green', alpha=0.4 , label='value2')
plt.legend()
# Add title and axis names
plt.yticks(my_range, ordered_df['group'])
plt.title("Comparison of the value 1 and the value 2", loc='left')
plt.xlabel('Value of the variables')
plt.ylabel('Group')
# Show the graph
plt.show()
image.png
# Data
x = np.linspace(0, 2*np.pi, 100)
y = np.sin(x) + np.random.uniform(size=len(x)) - 0.2
# Create a color if the y axis value is equal or greater than 0
my_color = np.where(y>=0, 'orange', 'skyblue')
# The vertical plot is made using the vline function
plt.vlines(x=x, ymin=0, ymax=y, color=my_color, alpha=0.4)
plt.scatter(x, y, color=my_color, s=1, alpha=1)
# Add title and axis names
plt.title("Evolution of the value of ...", loc='left')
plt.xlabel('Value of the variable')
plt.ylabel('Group')
# Show the graph
plt.show()
image.png
