蟒蛇熊猫按小时计算

Question

I'm working with the following dataset with hourly counts (df): The datframe has 8784 rows (for the year 2016, hourly).

I'd like to see if there are daily trends (eg if there is an increase in the morning hours. For this i'd like to create a plot that has the hour of the day (from 0 to 24) on the x-axis and number of cyclists on the y axis (something like in the picture below from http://ofdataandscience.blogspot.co.uk/2013/03/capital-bikeshare-time-series-clustering.html ).

I experimented with differet ways of pivot , resample and set_index and plotting it with matplotlib, without success. In other words, i couldn't find a way to sum up every observation at a certain hour and then plot those for each weekday

Any ideas how to do this? Thanks in advance!

Answer 1

I think you can use groupby by hour and weekday and aggregate sum (or maybe mean ), last reshape by unstack and DataFrame.plot :

df = df.groupby([df['Date'].dt.hour, 'weekday'])['Cyclists'].sum().unstack().plot()

Solution with pivot_table :

df1 = df.pivot_table(index=df['Date'].dt.hour, 
                     columns='weekday', 
                     values='Cyclists', 
                     aggfunc='sum').plot()

Sample:

N = 200
np.random.seed(100)
rng = pd.date_range('2016-01-01', periods=N, freq='H')
df = pd.DataFrame({'Date': rng, 'Cyclists': np.random.randint(100, size=N)}) 
df['weekday'] = df['Date'].dt.weekday_name
print (df.head())
   Cyclists                Date weekday
0         8 2016-01-01 00:00:00  Friday
1        24 2016-01-01 01:00:00  Friday
2        67 2016-01-01 02:00:00  Friday
3        87 2016-01-01 03:00:00  Friday
4        79 2016-01-01 04:00:00  Friday

---

print (df.groupby([df['Date'].dt.hour, 'weekday'])['Cyclists'].sum().unstack())
weekday  Friday  Monday  Saturday  Sunday  Thursday  Tuesday  Wednesday
Date                                                                   
0           102      91       120      53        95       86         21
1           102      83       100      27        20       94         25
2           121      53       105      56        10       98         54
3           164      78        54      30         8       42          6
4           163       0        43      48        89       84         37
5            49      13       150      47        72       95         58
6            24      57        32      39        30       76         39
7           127      76       128      38        12       33         94
8            72       3        59      44        18       58         51
9           138      70        67      18        93       42         30
10           77       3         7      64        92       22         66
11          159      84        49      56        44        0         24
12          156      79        47      34        57       55         55
13           42      10        65      53         0       98         17
14          116      87        61      74        73       19         45
15          106      60        14      17        54       53         89
16           22       3        55      72        92       68         45
17          154      48        71      13        66       62         35
18           60      52        80      30        16       50         16
19           79      43         2      17         5       68         12
20           11      36        94      53        51       35         86
21          180       5        19      68        90       23         82
22          103      71        98      50        34        9         67
23           92      38        63      91        67       48         92

df.groupby([df['Date'].dt.hour, 'weekday'])['Cyclists'].sum().unstack().plot()

EDIT:

You can also convert wekkday to categorical for correct soting of columns by names of week:

names = [ 'Monday', 'Tuesday', 'Wednesday', 'Thursday','Friday', 'Saturday', 'Sunday']
df['weekday'] = df['weekday'].astype('category', categories=names, ordered=True)
df.groupby([df['Date'].dt.hour, 'weekday'])['Cyclists'].sum().unstack().plot()