我曾嘗試創(chuàng)建時(shí)間序列數(shù)據(jù)集的條形圖，但沒(méi)有成功。我嘗試將日期轉(zhuǎn)換為 Pandas Datetime 對(duì)象、Timestamp 對(duì)象、原始字符串、浮點(diǎn)數(shù)和整數(shù)。無(wú)論我做什么，我都會(huì)收到以下錯(cuò)誤：TypeError: float() argument must be a string or a number, not 'Timestamp'以下是一些產(chǎn)生錯(cuò)誤的最小示例：在這里，“日期”對(duì)象的類(lèi)型為 <class 'pandas._libs.tslibs.timestamps.Timestamp'>，所以我知道為什么這不起作用：import matplotlib.pylab as pltimport matplotlib.dates as mdatesimport seaborn as snsdef main(): path = 'Data/AQ+RX Counts.csv' df = pd.read_csv(path, parse_dates=['Date'], index_col=['Date']) weekly_df = df.resample('W').mean().reset_index() weekly_df['count'] = df['count'].resample('W').sum().reset_index() sns.barplot(x = 'Date', y='count', data = weekly_df) plt.show()main()然后我嘗試使日期浮動(dòng)，打算將它們格式化回之后的日期，但這仍然不起作用： dates = mdates.datestr2num(weekly_df.Date.astype(str)) weekly_df['n_dates'] = dates sns.barplot(x = 'n_dates', y='count', data = weekly_df) plt.show()我也試過(guò)使它們成為整數(shù)，但無(wú)濟(jì)于事： dates = mdates.datestr2num(weekly_df.Date.astype(str)) dates = dates.astype(int) dates = pd.Series(dates) weekly_df['n_dates'] = dates sns.barplot(x = 'n_dates', y='count', data = weekly_df) plt.show()我嘗試了許多其他變體，但都產(chǎn)生了相同的錯(cuò)誤。我什至將它與其他代碼進(jìn)行了比較，并驗(yàn)證了所有類(lèi)型都相同，并且比較代碼工作正常。我完全不知道從這里去哪里。
1 回答
冉冉說(shuō)
TA貢獻(xiàn)1877條經(jīng)驗(yàn) 獲得超1個(gè)贊
函數(shù)中的轉(zhuǎn)換是'count'從浮點(diǎn)數(shù)轉(zhuǎn)換為日期時(shí)間數(shù)據(jù)類(lèi)型。
使用發(fā)布的樣本數(shù)據(jù)
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
path = 'data/test.csv'
df = pd.read_csv(path, parse_dates=['Date'], index_col=['Date'])
# display(df)
WSA WSV WDV WSM SGT T2M T10M DELTA_T PBAR SRAD RH PM25 AQI count
Date
2015-01-01 1.070833 0.875000 132.958333 3.470833 35.391667 30.729167 30.625000 -0.116667 738.825000 72.666667 99.754167 24.808333 73.307931 0.0
2015-01-02 1.108696 0.939130 148.478261 3.734783 32.465217 34.391304 34.278261 -0.117391 738.347826 61.391304 100.013043 23.500000 64.150725 4.0
2015-01-03 1.017391 0.717391 168.043478 3.773913 42.717391 36.247826 36.160870 -0.095652 739.443478 49.608696 100.769565 20.460870 55.652711 0.0
2015-01-04 1.000000 0.600000 159.958333 3.850000 49.150000 38.887500 38.666667 -0.225000 741.500000 31.541667 101.479167 13.012500 46.835258 0.0
2015-01-05 1.033333 0.441667 137.000000 4.000000 57.566667 42.995833 42.945833 -0.050000 742.533333 44.583333 101.004167 16.654167 52.420271 4.0
2015-01-06 0.781818 0.559091 114.727273 3.654545 42.868182 40.740909 41.095455 0.368182 740.904545 48.272727 100.577273 21.954545 67.318339 6.0
2015-01-07 0.973913 0.830435 110.826087 3.956522 30.817391 40.365217 40.595652 0.221739 739.865217 60.043478 100.195652 24.456522 72.347251 6.0
2015-01-08 0.983333 0.825000 156.500000 4.208333 32.670833 41.520833 41.366667 -0.129167 736.350000 69.583333 99.958333 22.275000 65.770725 10.0
2015-01-09 0.958333 0.729167 133.708333 3.379167 39.645833 42.279167 42.158333 -0.116667 735.204167 60.416667 100.041667 19.370833 59.085129 10.0
2015-01-10 0.966667 0.758333 164.500000 3.675000 37.345833 42.962500 42.775000 -0.200000 734.287500 41.500000 100.120833 14.658333 49.314653 0.0
# resample mean
dfr = df.resample('W').mean()
# add the resampled sum to dfr
dfr['mean'] = df['count'].resample('W').sum()
# reset index
dfr = dfr.reset_index()
# display(dfr)
Date WSA WSV WDV WSM SGT T2M T10M DELTA_T PBAR SRAD RH PM25 AQI count mean
0 2015-01-04 1.049230 0.782880 152.359601 3.707382 39.931069 35.063949 34.932699 -0.138678 739.529076 53.802083 100.503986 20.445426 59.986656 1.0 4.0
1 2015-01-11 0.949566 0.690615 136.210282 3.812261 40.152457 41.810743 41.822823 0.015681 738.190794 54.066590 100.316321 19.894900 61.042728 6.0 36.0
# plot dfr
fig, ax = plt.subplots(figsize=(16, 10))
fig = sns.barplot(x='Date', y='count', data=dfr)
# configure the xaxis ticks from datetime to date
x_dates = dfr.Date.dt.strftime('%Y-%m-%d').sort_values().unique()
ax.set_xticklabels(labels=x_dates, rotation=90, ha='right')
plt.show()
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