如何绘制给定 DataFrame 的数据？

Question

这是我正在使用的主要 DataFrame：

     -   Date     Open    High    Low   Close
1. 01-08-2019 | 97.85 | 98.45 | 96.40 |97.25
2. 02-08-2019 | 97.15 | 98.95 | 96.75 |98.15
3. 05-08-2019 | 98.30 | 98.70 | 94.30 |95.65
4. 06-08-2019 | 95.75 | 97.75 | 95.20 |97.05
5. 07-08-2019 | 96.80 | 97.70 | 96.05 |96.90
6. 08-08-2019 | 97.40 | 98.90 | 96.55 |97.40
7. 09-08-2019 | 97.20 | 98.10 | 96.65 |97.30
8. 12-08-2019 | 97.20 | 97.25 | 93.40 |93.75
9. 13-08-2019 | 93.70 | 96.60 | 93.15 |96.35

我从上面的 DataFrame 中提取了每日、3 天、每周、每月数据的高、低、收盘价。如何在单张图中绘制每日最高低收盘价、每周最高价、最低价、收盘价和每月最高低收盘价？

每日高低收盘

      Date      High    Low     Close
0   2019-08-01  98.45   96.40   97.25  
1   2019-08-02  98.95   96.75   98.15  
2   2019-08-05  98.70   94.30   95.65  
3   2019-08-06  97.75   95.20   97.05  
4   2019-08-07  97.70   96.05   96.90  

每周最高点、最低点、收盘价

    Date        High    Low    Close  
0   2019-08-04  98.95   96.75   98.15  
1   2019-08-11  98.90   96.65   97.40  
2   2019-08-18  97.25   94.15   96.35  
3   2019-08-25  93.85   92.25   93.50  
4   2019-09-01  94.85   93.40   94.20  

月高低收盘

     Date        High    Low    Close
0   2019-08-31  98.95   96.75   98.15
1   2019-09-30  104.90  102.08  103.83
2   2019-10-31  102.90  100.15  102.05
3   2019-11-30  118.90  117.95  118.55
4   2019-12-31  138.40  135.85  135.90

Link to CSV file I am working with

Answer 1

这是我第一次创建具有多个 x 轴的图表，并且我做了很多研究。我有几个问题要回答，比如验证数据和改进 x 轴的显示。我是根据these answers定制的。

import pandas as pd
import numpy as np

df= pd.read_csv('../../../Downloads/US Coffee Aug19-Jul20 - US Coffee Aug19-Jul20.csv')
df['Date'] = pd.to_datetime(df['Date'])
weeks = df.resample('W-Mon', on='Date')[['High','Close']].agg('mean').reset_index().sort_values(by='Date')
months = df.resample('1M', on='Date')[['High','Close']].agg('mean').reset_index().sort_values(by='Date')

import matplotlib.pyplot as plt

fig = plt.figure(figsize=(16,9),dpi=144)
ax1 = fig.add_subplot(111)
ax2 = ax1.twiny()
ax3 = ax1.twiny()

weeks_tick_locations = weeks['Date']
months_tick_locations = months['Date']

ax1.plot(df['Date'], df['High'], lw=1, color='b', label='Daily High')
ax1.plot(df['Date'], df['Close'], lw=1, color='r', label='Daily Low')

ax2.plot(weeks['Date'], weeks['High'], lw=2, color='g', label='Week High')
ax2.plot(weeks['Date'], weeks['Close'], lw=2, color='y', label='Week Low')

ax3.plot(months['Date'], months['High'], lw=2, color='k', label='Month High')
ax3.plot(months['Date'], months['Close'], lw=2, color='m', label='Month Low')

# Move twinned axis ticks and label from top to bottom
ax2.xaxis.set_ticks_position("bottom")
ax2.xaxis.set_label_position("bottom")

# Offset the twin axis below the host
ax2.spines["bottom"].set_position(("axes", -0.1))

ax2.set_frame_on(True)
ax2.patch.set_visible(False)

for sp in iter(ax2.spines.values()):
    sp.set_visible(False)
ax2.spines["bottom"].set_visible(True)

ax2.set_xticks(weeks_tick_locations)
ax2.set_xticklabels(weeks_tick_locations)
ax2.set_xlabel('Week')

ax3.xaxis.set_ticks_position("bottom")
ax3.xaxis.set_label_position("bottom")

# Offset the twin axis below the host
ax3.spines["bottom"].set_position(("axes", -0.2))

ax3.set_frame_on(True)
ax3.patch.set_visible(False)

for sp in iter(ax3.spines.values()):
    sp.set_visible(False)
ax3.spines["bottom"].set_visible(True)

ax3.set_xticks(months_tick_locations)
ax3.set_xticklabels(months_tick_locations)
ax3.set_xlabel('Month')

fig.legend(loc='upper left', bbox_to_anchor=(0.88, 0.98), bbox_transform=ax1.transAxes,)
plt.show()

Answer 2

重复使用ax 对象在同一图中绘制：

import pandas as pd
import matplotlib.pyplot as plt

daily_df = pd.DataFrame([
   ["2019-08-01", 98.45, 96.40, 97.25],
   ["2019-08-02", 98.95, 96.75, 98.15],
   ["2019-08-05", 98.70, 94.30, 95.65],
   ["2019-08-06", 97.75, 95.20, 97.05],
   ["2019-08-07", 97.70, 96.05, 96.90]
], columns=["Date", "daily_High", "daily_Low", "daily_Close"]
)

weekly_df = pd.DataFrame([
   ["2019-08-04", 98.95, 96.75, 98.15],
   ["2019-08-11", 98.90, 96.65, 97.40],
   ["2019-08-18", 97.25, 94.15, 96.35],
   ["2019-08-25", 93.85, 92.25, 93.50],
   ["2019-09-01", 94.85, 93.40, 94.20]
], columns=["Date", "weekly_High", "weekly_Low", "weekly_Close"]
)

monthly_df = pd.DataFrame([
   ["2019-08-31", 98.95 , 96.75 , 98.15],
   ["2019-09-30", 104.90, 102.08, 103.83],
   ["2019-10-31", 102.90, 100.15, 102.05],
   ["2019-11-30", 118.90, 117.95, 118.55],
   ["2019-12-31", 138.40, 135.85, 135.90]
], columns=["Date", "monthly_High", "monthly_Low", "monthly_Close"]
)

daily_df["Date"] = pd.to_datetime(daily_df["Date"], format='%Y-%m-%d')
weekly_df["Date"] = pd.to_datetime(weekly_df["Date"], format='%Y-%m-%d')
monthly_df["Date"] = pd.to_datetime(monthly_df["Date"], format='%Y-%m-%d')

daily_df.set_index(["Date"], inplace=True)
weekly_df.set_index(["Date"], inplace=True)
monthly_df.set_index(["Date"], inplace=True)

ax = daily_df.plot()
weekly_df.plot(ax=ax)
monthly_df.plot(ax=ax)

plt.show()

Answer 3

记得删除“假”today 日期的变量，我必须这样做，因为这个数据集不是最新的

import datetime
import matplotlib.pyplot as plt
from datetime import date
from datetime import datetime
from datetime import timedelta

today1 = date.today() #run in up to date dataframe , change to 'today' and remove fake today below

today = '2019-08-06'
w_today = '2019-08-06'
m_today = '2019-08-06'

today  = datetime.strptime(today,'%Y-%m-%d')
today = today.date()
w_today  = datetime.strptime(w_today,'%Y-%m-%d')
w_today = w_today.date()
m_today  = datetime.strptime(m_today,'%Y-%m-%d')
m_today = m_today.date()


df= pd.read_csv('D:/stack_overflow/US Coffee Aug19-Jul20.csv')
df['Date']=pd.to_datetime(df['Date']).dt.date
# df['Date']= df['Date'].floor('D')

#df from today , each day DAILY
df_daily = df[
    df.Date>=today
]

df_daily = df_daily.reset_index(drop=True)

weekly = []
monthly = []

last_date = df_daily['Date'].iloc[-1]

while w_today < last_date:
    w_today+=timedelta(days=7)
    weekly.append(w_today)


while m_today < last_date:
    m_today+=timedelta(days=10)
    monthly.append(m_today)

df_weekly = df_daily[
    df_daily.Date.isin(weekly)
]

df_monthly = df_daily[
    df_daily.Date.isin(monthly)
]
fig, axs = plt.subplots(3)
axs[0].plot(df_daily['Date'],df_daily[['Open','High','Low' ,'Close']])
axs[0].set_title('Daily')
axs[1].plot(df_weekly['Date'],df_weekly[['Open','High','Low' ,'Close']])
axs[1].set_title('Weekly')
axs[2].plot(df_monthly['Date'],df_monthly[['Open','High','Low' ,'Close']])
axs[2].set_title('Monthly')

plt.show()