index value vs. flight (data range A row & E row ) - pandas

Question

I want to know the scatter plot of the sum of the flight fields per minute. My information is as follows
http://python2018.byethost10.com/flights.csv
My grammar is as follows
import matplotlib.pyplot as plt
import pandas as pd
import matplotlib
matplotlib.rcParams['font.sans-serif'] = ['Noto Serif CJK TC']
matplotlib.rcParams['font.family']='sans-serif'
Df=pd.read_csv('flights.csv')
Df["time_hour"] = pd.to_datetime(df['time_hour'])
grp = df.groupby(by=[df.time_hour.map(lambda x : (x.hour, x.minute))])
a=grp.sum()
plt.scatter(a.index, a['flight'], c='b', marker='o')
plt.xlabel('index value', fontsize=16)
plt.ylabel('flight', fontsize=16)
plt.title('scatter plot - index value vs. flight (data range A row & E row )', fontsize=20)
plt.show()
Produced the following error：
Produced the following error
Traceback (most recent call last):
File "I:/PycharmProjects/1223/raise1/char3.py", line 10, in
Plt.scatter(a.index, a['flight'], c='b', marker='o')
File "C:\ProgramData\Anaconda3\lib\site-packages\matplotlib\pyplot.py", line 3470, in scatter
Edgecolors=edgecolors, data=data, **kwargs)
File "C:\ProgramData\Anaconda3\lib\site-packages\matplotlib__init__.py", line 1855, in inner
Return func(ax, *args, **kwargs)
File "C:\ProgramData\Anaconda3\lib\site-packages\matplotlib\axes_axes.py", line 4320, in scatter
Alpha=alpha
File "C:\ProgramData\Anaconda3\lib\site-packages\matplotlib\collections.py", line 927, in init
Collection.init(self, **kwargs)
File "C:\ProgramData\Anaconda3\lib\site-packages\matplotlib\collections.py", line 159, in init
Offsets = np.asanyarray(offsets, float)
File "C:\ProgramData\Anaconda3\lib\site-packages\numpy\core\numeric.py", line 544, in asanyarray
Return array(a, dtype, copy=False, order=order, subok=True)
ValueError: setting an array element with a sequence.
How can I produce the following results? Thank you.
http://python2018.byethost10.com/image.png

Answer 1

Problem is in aggregation, in your code it return tuples in index.
Solution is convert time_dt column to strings HH:MM by Series.dt.strftime:
a = df.groupby(by=[df.time_hour.dt.strftime('%H:%M')]).sum()
All together:
import matplotlib.pyplot as plt
import pandas as pd
import matplotlib
matplotlib.rcParams['font.sans-serif'] = ['Noto Serif CJK TC']
matplotlib.rcParams['font.family']='sans-serif'
#first column is index and second clumn is parsed to datetimes
df=pd.read_csv('flights.csv', index_col=[0], parse_dates=[1])
a = df.groupby(by=[df.time_hour.dt.strftime('%H:%M')]).sum()
print (a)
year sched_dep_time flight air_time distance hour minute
time_hour
05:00 122793 37856 87445 11282.0 72838 366 1256
05:01 120780 44810 82113 11115.0 71168 435 1310
05:02 122793 52989 99975 11165.0 72068 515 1489
05:03 120780 57653 98323 10366.0 65137 561 1553
05:04 122793 67706 110230 10026.0 63118 661 1606
05:05 122793 75807 126426 9161.0 55371 742 1607
05:06 120780 82010 120753 10804.0 67827 799 2110
05:07 122793 90684 130339 8408.0 52945 890 1684
05:08 120780 93687 114415 10299.0 63271 922 1487
05:09 122793 101571 99526 11525.0 72915 1002 1371
05:10 122793 107252 107961 10383.0 70137 1056 1652
05:11 120780 111351 120261 10949.0 73350 1098 1551
05:12 122793 120575 135930 8661.0 57406 1190 1575
05:13 120780 118272 104763 7784.0 55886 1166 1672
05:14 122793 37289 109300 9838.0 63582 364 889
05:15 122793 42374 67193 11480.0 78183 409 1474
05:16 58377 22321 53424 4271.0 27527 216 721
plt.scatter(a.index, a['flight'], c='b', marker='o')
#rotate labels of x axis
plt.xticks(rotation=90)
plt.xlabel('index value', fontsize=16)
plt.ylabel('flight', fontsize=16)
plt.title('scatter plot - index value vs. flight (data range A row & E row )', fontsize=20)
plt.show()
Another solution is convert datetimes to times:
import matplotlib.pyplot as plt
import pandas as pd
import matplotlib
matplotlib.rcParams['font.sans-serif'] = 'Noto Serif CJK TC'
matplotlib.rcParams['font.family']='sans-serif'
df=pd.read_csv('flights.csv', index_col=[0], parse_dates=[1])
a = df.groupby(by=[df.time_hour.dt.time]).sum()
print (a)
year sched_dep_time flight air_time distance hour minute
time_hour
05:00:00 122793 37856 87445 11282.0 72838 366 1256
05:01:00 120780 44810 82113 11115.0 71168 435 1310
05:02:00 122793 52989 99975 11165.0 72068 515 1489
05:03:00 120780 57653 98323 10366.0 65137 561 1553
05:04:00 122793 67706 110230 10026.0 63118 661 1606
05:05:00 122793 75807 126426 9161.0 55371 742 1607
05:06:00 120780 82010 120753 10804.0 67827 799 2110
05:07:00 122793 90684 130339 8408.0 52945 890 1684
05:08:00 120780 93687 114415 10299.0 63271 922 1487
05:09:00 122793 101571 99526 11525.0 72915 1002 1371
05:10:00 122793 107252 107961 10383.0 70137 1056 1652
05:11:00 120780 111351 120261 10949.0 73350 1098 1551
05:12:00 122793 120575 135930 8661.0 57406 1190 1575
05:13:00 120780 118272 104763 7784.0 55886 1166 1672
05:14:00 122793 37289 109300 9838.0 63582 364 889
05:15:00 122793 42374 67193 11480.0 78183 409 1474
05:16:00 58377 22321 53424 4271.0 27527 216 721
plt.scatter(a.index, a['flight'], c='b', marker='o')
plt.xticks(rotation=90)
plt.xlabel('index value', fontsize=16)
plt.ylabel('flight', fontsize=16)
plt.title('scatter plot - index value vs. flight (data range A row & E row )', fontsize=20)
plt.show()