BUG: DataFrame.plot.hist and Series.plot.hist do not produce the same histogram for the same column

[trenton3983]

Pandas version checks

• I have checked that this issue has not already been reported.

• I have confirmed this bug exists on the latest version of pandas.

• I have confirmed this bug exists on the main branch of pandas.

Reproducible Example

import pandas as pd
import requests

# URL for data from University of California Irvine
url = 'https://archive.ics.uci.edu/ml/machine-learning-databases/00492/Metro_Interstate_Traffic_Volume.csv.gz'

file = 'Metro_Interstate_Traffic_Volume.csv.gz'

content = requests.get(url).content
with open(file, 'wb') as f:
    f.write(content)

df = pd.read_csv(file, compression='gzip')

  holiday    temp  rain_1h  snow_1h  clouds_all weather_main weather_description            date_time  traffic_volume
0     NaN  288.28      0.0      0.0          40       Clouds    scattered clouds  2012-10-02 09:00:00            5545
1     NaN  289.36      0.0      0.0          75       Clouds       broken clouds  2012-10-02 10:00:00            4516
2     NaN  289.58      0.0      0.0          90       Clouds     overcast clouds  2012-10-02 11:00:00            4767
3     NaN  290.13      0.0      0.0          90       Clouds     overcast clouds  2012-10-02 12:00:00            5026
4     NaN  291.14      0.0      0.0          75       Clouds       broken clouds  2012-10-02 13:00:00            4918

               temp       rain_1h       snow_1h    clouds_all  traffic_volume
count  48204.000000  48204.000000  48204.000000  48204.000000    48204.000000
mean     281.205870      0.334264      0.000222     49.362231     3259.818355
std       13.338232     44.789133      0.008168     39.015750     1986.860670
min        0.000000      0.000000      0.000000      0.000000        0.000000
25%      272.160000      0.000000      0.000000      1.000000     1193.000000
50%      282.450000      0.000000      0.000000     64.000000     3380.000000
75%      291.806000      0.000000      0.000000     90.000000     4933.000000
max      310.070000   9831.300000      0.510000    100.000000     7280.000000

Plotting

fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(15, 6))

# pandas.DataFrame.plot / pandas.DataFrame.plot.hist
df.plot(kind='hist', column='traffic_volume', legend=False, ax=ax1)
ax1.bar_label(ax1.containers[0])
ax1.set_title('pandas.DataFrame.plot does not\nseem to correctly determine df.traffic_volume.max()')

# pandas.Series.plot / pandas.Series.plot.hist
df.traffic_volume.plot(kind='hist', ax=ax2)
_ = ax2.bar_label(ax2.containers[0])
ax2.set_title('pandas.Series.plot produces the expected result')

Issue Description

The plot produced by pandas.DataFrame.plot does not match the plot produced by pandas.Series.plot. kind='hist' for both.

Also see Different results when plotting histogram using DataFrame.plot.hist and Series.plot.hist

df[['traffic_volume', 'clouds_all']].plot(kind='hist', column='traffic_volume') produces the expected plot
df[['traffic_volume']].plot(kind='hist', column='traffic_volume') produces the expected plot
df[['traffic_volume', 'rain_1h']].plot(kind='hist', column='traffic_volume') produces the incorrect plot.

It seems def _calculate_bins applies to the entire numeric portion of the DataFrame, even if column='traffic_volume' is specified.

Expected Behavior

The expected behavior is the plot produced by pandas.Series.plot or matplotlib.axes.Axes.hist

pandas.DataFrame.hist and pandas.Series.hist also both produce the expected plot.

fig, ax = plt.subplots()
ax.hist(df.traffic_volume, bins=10)
_ = ax.bar_label(ax.containers[0])

I expect def _calculate_bins to calculate only on the specified columns, if column= is used. The column parameter here states If passed, will be used to limit data to a subset of columns.

Installed Versions

INSTALLED VERSIONS

commit : 478d340
python : 3.11.3.final.0
python-bits : 64
OS : Windows
OS-release : 10
Version : 10.0.22621
machine : AMD64
processor : Intel64 Family 6 Model 167 Stepping 1, GenuineIntel
byteorder : little
LC_ALL : None
LANG : None
LOCALE : English_United States.1252

pandas : 2.0.0
numpy : 1.24.3
pytz : 2022.7
dateutil : 2.8.2
setuptools : 66.0.0
pip : 23.0.1
Cython : None
pytest : None
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : None
lxml.etree : 4.9.2
html5lib : 1.1
pymysql : None
psycopg2 : None
jinja2 : None
IPython : 8.12.0
pandas_datareader: 0.10.0
bs4 : 4.12.2
bottleneck : None
brotli :
fastparquet : None
fsspec : None
gcsfs : None
matplotlib : 3.7.1
numba : None
numexpr : None
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : None
pyreadstat : None
pyxlsb : None
s3fs : None
scipy : 1.10.1
snappy : None
sqlalchemy : None
tables : None
tabulate : None
xarray : None
xlrd : 2.0.1
zstandard : None
tzdata : 2023.3
qtpy : None
pyqt5 : None