我在下面有以下命令：datamax['Date'] = pd.to_datetime(datamax['Date'])datamax2015['Date'] = pd.to_datetime(datamax2015['Date'])datamax["day_of_year"] = datamax["Date"].dt.dayofyeardatamax2015["day_of_year"] = datamax["Date"].dt.dayofyeardatamaxappend = datamax2015.join(datamax,on="day_of_year",rsuffix="_new")upper = datamaxappend.loc[datamaxappend["Data_Value_new"]<datamaxappend["Data_Value"]]upper = upper.rename(columns={"Date_new":"Date","Data_Value_new":"Data_Value"})lower = lower.rename(columns={"Date_new":"Date","Data_Value_new":"Data_Value"})upper['Date'] = upper['Date'].map(lambda x: x.strftime("%d/%m/%Y, %H:%M:%S"))lower['Date'] = lower['Date'].map(lambda x: x.strftime("%d/%m/%Y, %H:%M:%S")) 但是，我不斷收到錯(cuò)誤消息：'DataFrame' 對(duì)象沒有屬性 'map'我對(duì)這是為什么感到困惑。有人可以幫我嗎？下方的數(shù)據(jù)框如下所示：上： Date ID Element Data_Value day_of_year Date \3 2015-01-04 USW00094889 TMAX 44 4 2005-01-05 4 2015-01-05 USW00094889 TMAX 28 5 2005-01-06 5 2015-01-06 USW00094889 TMAX 33 6 2005-01-07 14 2015-01-15 USW00094889 TMAX -5 15 2005-01-16 15 2015-01-16 USW00094889 TMAX 6 16 2005-01-17 ID_new Element_new Data_Value day_of_year_new 3 USW00094889 TMAX 33 5 4 USW00094889 TMAX 0 6 5 USW00094889 TMAX 6 7 14 USW00094889 TMAX -33 16 15 USW00094889 TMAX -50 17
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小怪獸愛吃肉
TA貢獻(xiàn)1852條經(jīng)驗(yàn) 獲得超1個(gè)贊
upper和lower數(shù)據(jù)框有兩列稱為Date. 您正在使用upper['Date'].
解決方案：將至少一列重命名為不同于日期的某項(xiàng)，然后將您的函數(shù)分別應(yīng)用于每一列。
翻翻過去那場雪
TA貢獻(xiàn)2065條經(jīng)驗(yàn) 獲得超14個(gè)贊
返回 2005 年至 2014 年期間一年中各天的創(chuàng)紀(jì)錄高溫和低溫記錄折線圖的 Python 代碼。每天創(chuàng)紀(jì)錄的最高氣溫和最低氣溫之間的區(qū)域應(yīng)該用陰影標(biāo)出。
然后，為 2015 年打破十年記錄（2005-2014 年）歷史高點(diǎn)或歷史低點(diǎn)的任何點(diǎn)（高點(diǎn)和低點(diǎn)）疊加 2015 年數(shù)據(jù)的散點(diǎn)圖。
刪除閏年日期（即 2 月 29 日）。
from datetime import datetime
import pandas as pd
import matplotlib.pyplot as plt
pd.set_option("display.max_rows",None,"display.max_columns",None)
data = pd.read_csv('data/C2A2_data/BinnedCsvs_d400/fb441e62df2d58994928907a91895ec62c2c42e6cd075c2700843b89.csv')
newdata = data[(data['Date'] >= '2005-01-01') & (data['Date'] <= '2014-12-12')]
datamax = newdata[newdata['Element']=='TMAX']
datamin = newdata[newdata['Element']=='TMIN']
datamax['Date'] = pd.to_datetime(datamax['Date'])
datamin['Date'] = pd.to_datetime(datamin['Date'])
datamax["day_of_year"] = datamax["Date"].dt.dayofyear
datamax = datamax.groupby('day_of_year').max()
datamin["day_of_year"] = datamin["Date"].dt.dayofyear
datamin = datamin.groupby('day_of_year').min()
datamax = datamax.reset_index()
datamin = datamin.reset_index()
datamin['Date'] = datamin['Date'].dt.strftime('%Y-%m-%d')
datamax['Date'] = datamax['Date'].dt.strftime('%Y-%m-%d')
datamax = datamax[~datamax['Date'].str.contains("02-29")]
datamin = datamin[~datamin['Date'].str.contains("02-29")]
breakoutdata = data[(data['Date'] > '2014-12-31')]
datamax2015 = breakoutdata[breakoutdata['Element']=='TMAX']
datamin2015 = breakoutdata[breakoutdata['Element']=='TMIN']
datamax2015['Date'] = pd.to_datetime(datamax2015['Date'])
datamin2015['Date'] = pd.to_datetime(datamin2015['Date'])
datamax2015["day_of_year"] = datamax2015["Date"].dt.dayofyear
datamax2015 = datamax2015.groupby('day_of_year').max()
datamin2015["day_of_year"] = datamin2015["Date"].dt.dayofyear
datamin2015 = datamin2015.groupby('day_of_year').min()
datamax2015 = datamax2015.reset_index()
datamin2015 = datamin2015.reset_index()
datamin2015['Date'] = datamin2015['Date'].dt.strftime('%Y-%m-%d')
datamax2015['Date'] = datamax2015['Date'].dt.strftime('%Y-%m-%d')
datamax2015 = datamax2015[~datamax2015['Date'].str.contains("02-29")]
datamin2015 = datamin2015[~datamin2015['Date'].str.contains("02-29")]
dataminappend = datamin2015.join(datamin,on="day_of_year",rsuffix="_new")
lower = dataminappend.loc[dataminappend["Data_Value_new"]>dataminappend["Data_Value"]]
datamaxappend = datamax2015.join(datamax,on="day_of_year",rsuffix="_new")
upper = datamaxappend.loc[datamaxappend["Data_Value_new"]<datamaxappend["Data_Value"]]
upper['Date'] = pd.to_datetime(upper['Date'])
lower['Date'] = pd.to_datetime(lower['Date'])
datamax['Date'] = pd.to_datetime(datamax['Date'])
datamin['Date'] = pd.to_datetime(datamin['Date'])
ax = plt.gca()
plt.plot(datamax['day_of_year'],datamax['Data_Value'],color='red')
plt.plot(datamin['day_of_year'],datamin['Data_Value'], color='blue')
plt.scatter(upper['day_of_year'],upper['Data_Value'],color='purple')
plt.scatter(lower['day_of_year'],lower['Data_Value'], color='cyan')
plt.ylabel("Temperature (degrees C)",color='navy')
plt.xlabel("Day of the year",color='navy',labelpad=15)
plt.title('Record high and low temperatures by day between 2005-2014)', alpha=1.0,color='brown',y=1.08)
ax.legend(loc='upper center', bbox_to_anchor=(0.5, -0.35),fancybox=False,labels=['Record high','Record low'])
plt.xticks(rotation=30)
plt.fill_between(range(len(datamax['Date'])), datamax['Data_Value'], datamin['Data_Value'],color='yellow',alpha=0.8)
plt.show()
我已使用 Datamin['Date'] = datamin['Date'].dt.strftime('%Y-%m-%d') 將“日期”列轉(zhuǎn)換為字符串。
然后我使用 upper['Date'] = pd.to_datetime(upper['Date']) 將其轉(zhuǎn)換回 'datetime' 格式
然后我使用“年份日期”作為 x 值。
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