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本文小編為大家詳細(xì)介紹“怎么使用Python seaborn barplot畫圖”，內(nèi)容詳細(xì)，步驟清晰，細(xì)節(jié)處理妥當(dāng)，希望這篇“怎么使用Python seaborn barplot畫圖”文章能幫助大家解決疑惑，下面跟著小編的思路慢慢深入，一起來學(xué)習(xí)新知識吧。
import seaborn as sns
import matplotlib.pyplot as plt
import numpy as np
sns.set_theme()
df = sns.load_dataset("tips")
#默認(rèn)畫條形圖
sns.barplot(x="day",y="total_bill",data=df)
plt.show()
#計算平均值看是否和條形圖的高度一致
print(df.groupby("day").agg({"total_bill":[np.mean]}))
print(df.groupby("day").agg({"total_bill":[np.std]}))
# 注意這個地方error bar顯示并不是標(biāo)準(zhǔn)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
# 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')
ax=sns.barplot(x="day",y="total_bill",data=df,capsize=1.0) plt.show()
import seaborn as sns
import matplotlib.pyplot as plt
sns.set_theme()
df = sns.load_dataset("tips")
#默認(rèn)畫條形圖
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() # 顯示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)
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()
import seaborn as sns
import matplotlib.pyplot as plt
sns.set_theme()
df = sns.load_dataset("tips")
#默認(rèn)畫條形圖
sns.barplot(x="day",y="total_bill",data=df,ci="sd",capsize=1.0)## 注意這個ci參數(shù)
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
import seaborn as sns
import matplotlib.pyplot as plt
sns.set_theme()
df = sns.load_dataset("tips")
#默認(rèn)畫條形圖
sns.barplot(x="day",y="total_bill",data=df,ci=68,capsize=1.0)## 注意這個ci參數(shù)
plt.show()
import seaborn as sns
import matplotlib.pyplot as plt
sns.set_theme()
df = sns.load_dataset("tips")
#默認(rèn)畫條形圖
sns.barplot(x="day",y="total_bill",data=df,ci=95)
plt.show()
#計算平均值看是否和條形圖的高度一致
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
#計算平均值看是否和條形圖的高度一致
df = sns.load_dataset("tips")
print("="*20)
print(df.groupby("day").agg({"total_bill":[np.mean]})) # 分組求均值
print("="*20)
print(df.groupby("day").agg({"total_bill":[np.std]})) # 分組求標(biāo)準(zhǔn)差
print("="*20)
print(df.groupby("day").agg({"total_bill":"nunique"})) # 這里統(tǒng)計的是不同的數(shù)目
print("="*20)
print(df.groupby("day").agg({"total_bill":"count"})) # 這里統(tǒng)計的是每個分組樣本的數(shù)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 ====================
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
df = sns.load_dataset("tips")
#默認(rèn)畫條形圖
def custom1(x):
m = x.mean()
s = x.std()
n = x.count()# 統(tǒng)計個數(shù)
#print(n)
return m+1.96*s/np.sqrt(n)
def custom2(x):
m = x.mean()
s = x.std()
n = x.count()# 統(tǒng)計個數(shù)
#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]})) # 分組求標(biāo)準(zhǔn)差
sns.barplot(x="day",y="total_bill",data=df,ci=68)
plt.show()
print(df.groupby("day").agg({"total_bill":[np.std,custom2]})) #[外鏈圖片轉(zhuǎn)存失敗,源站可能有防盜鏈機制,建議將圖片保存下來直接上傳(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
[外鏈圖片轉(zhuǎn)存失敗,源站可能有防盜鏈機制,建議將圖片保存下來直接上傳(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 22.423114
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
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
#estimator 指定條形圖高度使用相加的和
sns.barplot(x="day",y="total_bill",data=df,estimator=np.sum)
plt.show()
#計算想加和看是否和條形圖的高度一致
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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