Python seaborn barplot画图案例

2024-04-02 19:04:59 313人浏览独家记忆

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摘要

目录默认barplot使用案例修改capsize显示error bar的值annotata error barerror bar选取sd设置置信区间(68)设置置信区间(95)dat

默认barplot
使用案例
修改capsize
显示error bar的值
annotata error bar
error bar选取sd
设置置信区间(68)
设置置信区间(95)
dataframe aggregate函数使用
dataframe aggregate 自定义函数
dataframe aggregate 自定义函数2
seaborn显示网格
seaborn设置刻度
使用其他estaimator

默认barplot

import seaborn as sns
import matplotlib.pyplot as plt 
import numpy as np 

sns.set_theme(style="whitegrid")
df = sns.load_dataset("tips")
#默认画条形图
sns.barplot(x="day",y="total_bill",data=df)
plt.show()

#计算平均值看是否和条形图的高度一致
print(df.groupby("day").agg({"total_bill":[np.mean]}))
print(df.groupby("day").agg({"total_bill":[np.std]}))
# 注意这个地方error bar显示并不是标准差

     total_bill
           mean
day
Thur  17.682742
Fri   17.151579
Sat   20.441379
Sun   21.410000
     total_bill
            std
day
Thur   7.886170
Fri    8.302660
Sat    9.480419
Sun    8.832122

使用案例

# import libraries
import seaborn as sns
import numpy as np
import matplotlib.pyplot as plt
# load dataset
tips = sns.load_dataset("tips")
# Set the figure size
plt.figure(figsize=(14, 8))
# plot a bar chart
ax = sns.barplot(x="day", y="total_bill", data=tips, estimator=np.mean, ci=85, capsize=.2, color='lightblue')

修改capsize

ax=sns.barplot(x="day",y="total_bill",data=df,capsize=1.0)
plt.show()

显示error bar的值

import seaborn as sns
import matplotlib.pyplot as plt 
sns.set_theme(style="whitegrid")
df = sns.load_dataset("tips")
#默认画条形图
ax=sns.barplot(x="day",y="total_bill",data=df)
plt.show()
for p in ax.lines:
    width = p.get_linewidth()
    xy = p.get_xydata() # 显示error bar的值
    print(xy)
    print(width)
    print(p)

[[ 0.         15.85041935]
 [ 0.         19.64465726]]
2.7
Line2D(_line0)
[[ 1.         13.93096053]
 [ 1.         21.38463158]]
2.7
Line2D(_line1)
[[ 2.         18.57236207]
 [ 2.         22.40351437]]
2.7
Line2D(_line2)
[[ 3.         19.66244737]
 [ 3.         23.50109868]]
2.7
Line2D(_line3)

annotata error bar

fig, ax = plt.subplots(figsize=(8, 6))
sns.barplot(x='day', y='total_bill', data=df, capsize=0.2, ax=ax)

# show the mean
for p in ax.patches:
    h, w, x = p.get_height(), p.get_width(), p.get_x()
    xy = (x + w / 2., h / 2)
    text = f'Mean:\n{h:0.2f}'
    ax.annotate(text=text, xy=xy, ha='center', va='center')

ax.set(xlabel='day', ylabel='total_bill')
plt.show()

error bar选取sd

import seaborn as sns
import matplotlib.pyplot as plt 
sns.set_theme(style="whitegrid")
df = sns.load_dataset("tips")
#默认画条形图
sns.barplot(x="day",y="total_bill",data=df,ci="sd",capsize=1.0)## 注意这个ci参数
plt.show()

print(df.groupby("day").agg({"total_bill":[np.mean]}))
print(df.groupby("day").agg({"total_bill":[np.std]}))

     total_bill
           mean
day
Thur  17.682742
Fri   17.151579
Sat   20.441379
Sun   21.410000
     total_bill
            std
day
Thur   7.886170
Fri    8.302660
Sat    9.480419
Sun    8.832122

设置置信区间(68)

import seaborn as sns
import matplotlib.pyplot as plt 
sns.set_theme(style="whitegrid")
df = sns.load_dataset("tips")
#默认画条形图
sns.barplot(x="day",y="total_bill",data=df,ci=68,capsize=1.0)## 注意这个ci参数
plt.show()

设置置信区间(95)

import seaborn as sns
import matplotlib.pyplot as plt 
sns.set_theme(style="whitegrid")
df = sns.load_dataset("tips")
#默认画条形图
sns.barplot(x="day",y="total_bill",data=df,ci=95)
plt.show()

#计算平均值看是否和条形图的高度一致
print(df.groupby("day").agg({"total_bill":[np.mean]}))

     total_bill
           mean
day
Thur  17.682742
Fri   17.151579
Sat   20.441379
Sun   21.410000

dataframe aggregate函数使用

#计算平均值看是否和条形图的高度一致
df = sns.load_dataset("tips")
print("="*20)
print(df.groupby("day").agg({"total_bill":[np.mean]})) # 分组求均值
print("="*20)
print(df.groupby("day").agg({"total_bill":[np.std]})) # 分组求标准差
print("="*20)
print(df.groupby("day").agg({"total_bill":"nunique"})) # 这里统计的是不同的数目
print("="*20)
print(df.groupby("day").agg({"total_bill":"count"})) # 这里统计的是每个分组样本的数量
print("="*20)
print(df["day"].value_counts())
print("="*20)

====================
     total_bill
           mean
day
Thur  17.682742
Fri   17.151579
Sat   20.441379
Sun   21.410000
====================
     total_bill
            std
day
Thur   7.886170
Fri    8.302660
Sat    9.480419
Sun    8.832122
====================
      total_bill
day
Thur          61
Fri           18
Sat           85
Sun           76
====================
      total_bill
day
Thur          62
Fri           19
Sat           87
Sun           76
====================
Sat     87
Sun     76
Thur    62
Fri     19
Name: day, dtype: int64
====================

dataframe aggregate 自定义函数

import numpy as np
import pandas as pd

df = pd.DataFrame({'Buy/Sell': [1, 0, 1, 1, 0, 1, 0, 0],
                   'Trader': ['A', 'A', 'B', 'B', 'B', 'C', 'C', 'C']})
print(df)
def cateGorize(x):
    m = x.mean()
    return 1 if m > 0.5 else 0 if m < 0.5 else np.nan
result = df.groupby(['Trader'])['Buy/Sell'].agg([categorize, 'sum', 'count'])
result = result.rename(columns={'categorize' : 'Buy/Sell'})
result

   Buy/Sell Trader
0         1      A
1         0      A
2         1      B
3         1      B
4         0      B
5         1      C
6         0      C
7         0      C

dataframe aggregate 自定义函数2

df = sns.load_dataset("tips")
#默认画条形图

def custom1(x):
    m = x.mean()
    s = x.std()
    n = x.count()# 统计个数
    #print(n)
    return m+1.96*s/np.sqrt(n)
def custom2(x):
    m = x.mean()
    s = x.std()
    n = x.count()# 统计个数
    #print(n)
    return m+s/np.sqrt(n)
sns.barplot(x="day",y="total_bill",data=df,ci=95)
plt.show()
print(df.groupby("day").agg({"total_bill":[np.std,custom1]})) # 分组求标准差

sns.barplot(x="day",y="total_bill",data=df,ci=68)
plt.show()
print(df.groupby("day").agg({"total_bill":[np.std,custom2]})) #

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     total_bill
            std    custom1
day
Thur   7.886170  19.645769
Fri    8.302660  20.884910
Sat    9.480419  22.433538
Sun    8.832122  23.395703

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     total_bill
            std    custom2
day
Thur   7.886170  18.684287
Fri    8.302660  19.056340
Sat    9.480419  21.457787
Sun    8.832122  22.423114

seaborn显示网格

ax=sns.barplot(x="day",y="total_bill",data=df,ci=95)
ax.yaxis.grid(True) # Hide the horizontal gridlines
ax.xaxis.grid(True) # Show the vertical gridlines

seaborn设置刻度

fig, ax = plt.subplots(figsize=(10, 8))
sns.barplot(x="day",y="total_bill",data=df,ci=95,ax=ax)
ax.set_yticks([i for i in range(30)])
ax.yaxis.grid(True) # Hide the horizontal gridlines

使用其他estaimator

#estimator 指定条形图高度使用相加的和
sns.barplot(x="day",y="total_bill",data=df,estimator=np.sum)
plt.show()
#计算想加和看是否和条形图的高度一致
print(df.groupby("day").agg({"total_bill":[np.sum]}))
'''
     total_bill
            sum
day
Fri      325.88
Sat     1778.40
Sun     1627.16
Thur    1096.33
'''