Python seaborn barplot画图案例

目录

• 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
'''

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