我正在嘗試?yán)L制股票市場(chǎng)圖表時(shí)間序列與收盤(pán)價(jià)以及時(shí)間序列與交易量。x 軸以某種方式顯示 1970 年的時(shí)間下面是圖和代碼https://i.stack.imgur.com/THvGM.png 代碼是：import pandas as pdimport matplotlib.pyplot as pltimport matplotlib.dates as mdatespd_data = pd.DataFrame(data, columns=['id', 'symbol', 'volume', 'high', 'low', 'open', 'datetime','close','datetime_utc','created_at'])pd_data['DOB'] = pd.to_datetime(pd_data['datetime_utc']).dt.strftime('%Y-%m-%d') pd_data.set_index('DOB')print(pd_data)print(pd_data.dtypes)ax=pd_data.plot(x='DOB',y='close',kind = 'line')ax.set_ylabel("price")#ax.pd_data['volume'].plot(secondary_y=True, kind='bar')ax1=pd_data.plot(y='volume',secondary_y=True, ax=ax,kind='bar')ax1.set_ylabel('Volumne')# Choose your xtick format stringdate_fmt = '%d-%m-%y'date_formatter = mdates.DateFormatter(date_fmt)ax1.xaxis.set_major_formatter(date_formatter)# set monthly locatorax1.xaxis.set_major_locator(mdates.MonthLocator(interval=1))# set font and rotation for date tick labelsplt.gcf().autofmt_xdate()plt.show()還獨(dú)立嘗試了這兩個(gè)圖，沒(méi)有ax=axax=pd_data.plot(x='DOB',y='close',kind = 'line')ax.set_ylabel("price")ax1=pd_data.plot(y='volume',secondary_y=True,kind='bar')ax1.set_ylabel('Volumne')那么價(jià)格圖正確顯示年份，而成交量圖顯示 1970 年如果我交換它們ax1=pd_data.plot(y='volume',secondary_y=True,kind='bar')ax1.set_ylabel('Volumne')ax=pd_data.plot(x='DOB',y='close',kind = 'line')ax.set_ylabel("price")現(xiàn)在，成交量圖正確顯示了年份，而價(jià)格圖則顯示了 1970 年的年份我嘗試刪除 secondary_y 并將條更改為行。但沒(méi)有運(yùn)氣不知何故，第一張圖之后的 pandas 數(shù)據(jù)正在改變年份。
3 回答
慕斯王
TA貢獻(xiàn)1864條經(jīng)驗(yàn) 獲得超2個(gè)贊
我不建議繪制包含如此多條形圖的條形圖。
此答案解釋了xtick 標(biāo)簽存在問(wèn)題的原因以及如何解決該問(wèn)題。
繪圖與pandas.DataFrame.plot工作沒(méi)有問(wèn)題.set_major_locator
測(cè)試于python 3.8.11, pandas 1.3.2,matplotlib 3.4.2
import pandas as pd
import matplotlib.pyplot as plt
import matplotlib.dates as mdates
import yfinance as yf # conda install -c conda-forge yfinance or pip install yfinance --upgrade --no-cache-dir
# download data
df = yf.download('amzn', start='2015-02-21', end='2021-04-27')
# plot
ax = df.plot(y='Close', color='magenta', ls='-.', figsize=(10, 6), ylabel='Price ($)')
ax1 = df.plot(y='Volume', secondary_y=True, ax=ax, alpha=0.5, rot=0, lw=0.5)
ax1.set(ylabel='Volume')
# format
date_fmt = '%d-%m-%y'
years = mdates.YearLocator() # every year
yearsFmt = mdates.DateFormatter(date_fmt)
ax.xaxis.set_major_locator(years)
ax.xaxis.set_major_formatter(yearsFmt)
plt.setp(ax.get_xticklabels(), ha="center")
plt.show()
https://i.stack.imgur.com/8MHUi.png
為什么 OP x-tick 標(biāo)簽從 1970 年開(kāi)始？
條形圖位置的索引為 0（使用 pandas），0 對(duì)應(yīng)于 1970
請(qǐng)參閱Pandas 條形圖更改日期格式
大多數(shù)帶有條形圖的解決方案只是將標(biāo)簽重新格式化為適當(dāng)?shù)娜掌跁r(shí)間，但是這是裝飾性的，并且不會(huì)對(duì)齊線圖和條形圖之間的位置
這個(gè)答案的解決方案 2顯示了如何更改刻度定位器，但在
plt.bar可以使用時(shí)確實(shí)不值得額外的代碼。
print(pd.to_datetime(ax1.get_xticks()))
DatetimeIndex([ '1970-01-01 00:00:00',
'1970-01-01 00:00:00.000000001',
'1970-01-01 00:00:00.000000002',
'1970-01-01 00:00:00.000000003',
...
'1970-01-01 00:00:00.000001552',
'1970-01-01 00:00:00.000001553',
'1970-01-01 00:00:00.000001554',
'1970-01-01 00:00:00.000001555'],
dtype='datetime64[ns]', length=1556, freq=None)
ax = df.plot(y='Close', color='magenta', ls='-.', figsize=(10, 6), ylabel='Price ($)')
print(ax.get_xticks())
ax1 = df.plot(y='Volume', secondary_y=True, ax=ax, kind='bar')
print(ax1.get_xticks())
ax1.set_xlim(0, 18628.)
date_fmt = '%d-%m-%y'
years = mdates.YearLocator() # every year
yearsFmt = mdates.DateFormatter(date_fmt)
ax.xaxis.set_major_locator(years)
ax.xaxis.set_major_formatter(yearsFmt)
[out]:
[16071. 16436. 16801. 17167. 17532. 17897. 18262. 18628.] ← ax tick locations
[ 0 1 2 ... 1553 1554 1555] ← ax1 tick locations
https://i.stack.imgur.com/ytQfd.png
plt.bar條形圖位置根據(jù)日期時(shí)間進(jìn)行索引
ax = df.plot(y='Close', color='magenta', ls='-.', figsize=(10, 6), ylabel='Price ($)', rot=0)
plt.setp(ax.get_xticklabels(), ha="center")
print(ax.get_xticks())
ax1 = ax.twinx()
ax1.bar(df.index, df.Volume)
print(ax1.get_xticks())
date_fmt = '%d-%m-%y'
years = mdates.YearLocator() # every year
yearsFmt = mdates.DateFormatter(date_fmt)
ax.xaxis.set_major_locator(years)
ax.xaxis.set_major_formatter(yearsFmt)
[out]:
[16071. 16436. 16801. 17167. 17532. 17897. 18262. 18628.]
[16071. 16436. 16801. 17167. 17532. 17897. 18262. 18628.]
https://i.stack.imgur.com/4MxzW.png
sns.barplot(x=df.index, y=df.Volume, ax=ax1)xtick位置為，[ 0 1 2 ... 1553 1554 1555]因此條形圖和線圖未對(duì)齊。
瀟瀟雨雨
TA貢獻(xiàn)1833條經(jīng)驗(yàn) 獲得超4個(gè)贊
我找不到 1970 的原因，而是使用 matplotlib.pyplot 進(jìn)行繪圖而不是間接使用 pandas 并傳遞 datatime 數(shù)組而不是 pandas
所以下面的代碼有效
import matplotlib.pyplot as plt
import matplotlib.dates as mdates
import pandas as pd
import datetime as dt
import numpy as np
pd_data = pd.read_csv("/home/stockdata.csv",sep='\t')
pd_data['DOB'] = pd.to_datetime(pd_data['datetime2']).dt.strftime('%Y-%m-%d')
dates=[dt.datetime.strptime(d,'%Y-%m-%d').date() for d in pd_data['DOB']]
plt.gca().xaxis.set_major_formatter(mdates.DateFormatter('%m/%d/%Y'))
plt.gca().xaxis.set_major_locator(mdates.MonthLocator(interval=2))
plt.bar(dates,pd_data['close'],align='center')
plt.gca().xaxis.set_minor_locator(plt.MultipleLocator(1))
plt.gcf().autofmt_xdate()
plt.show()
我創(chuàng)建了一個(gè)日期時(shí)間格式的日期數(shù)組。如果我用它制作圖表，那么日期將不再顯示為 1970 年
open high low close volume datetime datetime2
35.12 35.68 34.79 35.58 1432995 1244385200000 2012-6-15 10:30:00
35.69 36.02 35.37 35.78 1754319 1244371600000 2012-6-16 10:30:00
35.69 36.23 35.59 36.23 3685845 1245330800000 2012-6-19 10:30:00
36.11 36.52 36.03 36.32 2635777 1245317200000 2012-6-20 10:30:00
36.54 36.6 35.8 35.9 2886412 1245303600000 2012-6-21 10:30:00
36.03 36.95 36.0 36.09 3696278 1245390000000 2012-6-22 10:30:00
36.5 37.27 36.18 37.11 2732645 1245376400000 2012-6-23 10:30:00
36.98 37.11 36.686 36.83 1948411 1245335600000 2012-6-26 10:30:00
36.67 37.06 36.465 37.05 2557172 1245322000000 2012-6-27 10:30:00
37.06 37.61 36.77 37.52 1780126 1246308400000 2012-6-28 10:30:00
37.47 37.77 37.28 37.7 1352267 1246394800000 2012-6-29 10:30:00
37.72 38.1 37.68 37.76 2194619 1246381200000 2012-6-30 10:30:00
我得到的情節(jié)是
https://i.stack.imgur.com/lrtEN.png
幕布斯6054654
TA貢獻(xiàn)1876條經(jīng)驗(yàn) 獲得超7個(gè)贊
首先，您必須指定日期格式，然后在底部指定 Y 軸以僅顯示“%Y”年
# Convert 'Filing date' to a datetime format (assuming it's not already)
df_transposed['Filing date'] = pd.to_datetime(df_transposed['Filing date'], format='%Y-%m-%d') # Specify the format
.
.
.
# Create a figure with two y-axes
fig, ax1 = plt.subplots(figsize=(16, 6))
# Create a bar chart for Total Revenue against Filing Date on the first y-axis
ax1.bar(df_filtered['Filing date'], df_filtered['Total Revenue'], color='b', align='center', alpha=0.5, width=50) # Adjust bar width as needed
ax1.set_xlabel('Year')
ax1.set_ylabel('Total Revenue in Billions')
ax1.set_title('Total Revenue Over Years Bar Chart and Line Chart')
ax1.tick_params(axis='x', rotation=45)
# Adjust the transparency (alpha) of the horizontal grid lines to make them more faint
ax1.grid(axis='x', alpha=0.5)
# Create a line chart for Total Revenue against Filing Date on the second y-axis
ax2 = ax1.twinx()
ax2.plot(df_filtered['Filing date'], df_filtered['Total Revenue'], '-', color='r')
ax2.set_ylabel('Total Revenue in Billions (Line Chart)')
# Adjust the y-axis limits to match between both axes
min_y = min(ax1.get_ylim()[0], ax2.get_ylim()[0])
max_y = max(ax1.get_ylim()[1], ax2.get_ylim()[1])
ax1.set_ylim(min_y, max_y)
ax2.set_ylim(min_y, max_y)
# Set the x-axis to display only years
plt.gca().xaxis.set_major_formatter(plt.matplotlib.dates.DateFormatter('%Y'))
# Show the combined chart
plt.tight_layout()
plt.show()
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