Seaborn 条形 Plot，日期为 X 轴

Question

I have tried unsuccessfully to create a bar plot of a time series dataset. I have tried converting the dates to Pandas Datetime objects, Timestamp Objects, primitive strings, floats, and ints. No matter what I do, I get the following error: TypeError: float() argument must be a string or a number, not 'Timestamp' Here are a few minimal examples that produce the error:

1. Here, the 'Date' object is of type <class 'pandas._libs.tslibs.timestamps.Timestamp'>, so I know why this doesn't work:

import matplotlib.pylab as plt
import matplotlib.dates as mdates
import seaborn as sns


def 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()

2. I then tried making the dates floats, intending to format them back to dates after, but this still doesnt work:

   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()

3. I also tried making them integers, to no avail:

    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()

I've tried many other variations, and all produce the same error. I've even compared it to other code and verified that all the types are identical, and the comparison code works fine. I am at a complete loss of where to go from here.

Dataframe:

Date,WSA,WSV,WDV,WSM,SGT,T2M,T10M,DELTA_T,PBAR,SRAD,RH,PM25,AQI,count
2015-01-01,1.0708333333333335,0.8750000000000001,132.95833333333334,3.4708333333333337,35.39166666666667,30.72916666666667,30.625,-0.11666666666666667,738.8249999999998,72.66666666666667,99.75416666666666,24.80833333333333,73.30793131580873,0.0
2015-01-02,1.1086956521739129,0.9391304347826086,148.47826086956522,3.734782608695653,32.46521739130434,34.39130434782609,34.27826086956521,-0.11739130434782602,738.3478260869565,61.39130434782609,100.01304347826084,23.500000000000004,64.15072523318715,4.0
2015-01-03,1.0173913043478258,0.7173913043478259,168.04347826086956,3.773913043478261,42.71739130434783,36.24782608695652,36.160869565217396,-0.09565217391304348,739.4434782608695,49.60869565217392,100.76956521739132,20.460869565217394,55.65271063058384,0.0
2015-01-04,1.0,0.6,159.95833333333334,3.85,49.15,38.8875,38.66666666666666,-0.225,741.5000000000001,31.54166666666667,101.47916666666669,13.012499999999998,46.835258118800965,0.0
2015-01-05,1.0333333333333334,0.4416666666666667,137.0,4.0,57.56666666666666,42.99583333333333,42.94583333333333,-0.04999999999999995,742.5333333333333,44.58333333333334,101.00416666666666,16.654166666666665,52.420271225456766,4.0
2015-01-06,0.7818181818181817,0.5590909090909091,114.72727272727272,3.654545454545455,42.86818181818182,40.7409090909091,41.09545454545454,0.36818181818181817,740.9045454545453,48.27272727272727,100.57727272727274,21.954545454545453,67.31833852518514,6.0
2015-01-07,0.9739130434782608,0.8304347826086954,110.82608695652172,3.956521739130436,30.817391304347833,40.36521739130435,40.59565217391304,0.22173913043478266,739.8652173913043,60.04347826086956,100.19565217391305,24.456521739130434,72.3472505968891,6.0
2015-01-08,0.9833333333333336,0.8250000000000001,156.5,4.208333333333333,32.67083333333333,41.520833333333336,41.36666666666667,-0.12916666666666668,736.35,69.58333333333333,99.95833333333331,22.274999999999995,65.77072473472253,10.0
2015-01-09,0.9583333333333331,0.7291666666666669,133.70833333333334,3.3791666666666664,39.645833333333336,42.279166666666654,42.15833333333333,-0.11666666666666665,735.2041666666665,60.41666666666666,100.04166666666669,19.370833333333334,59.08512936837911,10.0
2015-01-10,0.9666666666666668,0.7583333333333336,164.5,3.675,37.34583333333333,42.96250000000001,42.775,-0.2,734.2875,41.5,100.12083333333337,14.658333333333335,49.31465266245389,0.0

Answer 1

• The transformation in the function is converting 'count' from floats to a datetime dtype.
• Using the posted sample data

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()