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 21.410000dataframe 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'})
resultBuy/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]})) #[外链图片转存失败,源站可能有防盗链机制,建议将图片保存下来直接上传(img-pkCx72ui-1658379974318)(output_24_0.png)]
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[外链图片转存失败,源站可能有防盗链机制,建议将图片保存下来直接上传(img-GFyIePmW-1658379974318)(output_24_2.png)]
total_bill
std custom2
day
Thur 7.886170 18.684287
Fri 8.302660 19.056340
Sat 9.480419 21.457787
Sun 8.832122 22.423114seaborn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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