提问人：Explorer 提问时间：10/18/2023 最后编辑：Trenton McKinneyExplorer 更新时间：10/18/2023 访问量：63
绘制列中唯一值的时间序列折线图
plotting timeseries line graph for unique values in a column
问：
我正在尝试为数据集中的所有唯一键绘制时间序列图，下面是我的数据集，我有 7 个唯一键，我正在尝试在 x 轴上绘制event_date并在 y 轴上绘制折线图以计数。
我正在遍历每个唯一的键，并尝试绘制日期与计数，但出现以下错误。根据错误，我无法理解为什么 y 的形状是 （1,0）
import matplotlib.pyplot as plt
import pandas as pd
df_pandas = df.toPandas()
df_pandas.event_date = pd.to_datetime(df_pandas.event_date) # converting object to pandas datetime
colors = ['r', 'g', 'b', 'y', 'k', 'c', 'm']
for i, k in enumerate(df_pandas.key.unique()):
plt.plot(df_pandas[df_pandas.key == k].event_date, df_pandas[df_pandas.key == k].count, '-o', label=k, c=colors[i])
plt.gcf().autofmt_xdate() ## Rotate X-axis so you can see dates clearly without overlap
plt.legend() ## Show legend
Traceback (most recent call last):
File "/tmp/1697561012681-0/zeppelin_python.py", line 153, in <module>
exec(code, _zcUserQueryNameSpace)
File "<stdin>", line 13, in <module>
File "/usr/local/lib64/python3.7/site-packages/matplotlib/pyplot.py", line 2813, in plot
is not None else {}), **kwargs)
File "/usr/local/lib64/python3.7/site-packages/matplotlib/__init__.py", line 1805, in inner
return func(ax, *args, **kwargs)
File "/usr/local/lib64/python3.7/site-packages/matplotlib/axes/_axes.py", line 1603, in plot
for line in self._get_lines(*args, **kwargs):
File "/usr/local/lib64/python3.7/site-packages/matplotlib/axes/_base.py", line 393, in _grab_next_args
yield from self._plot_args(this, kwargs)
File "/usr/local/lib64/python3.7/site-packages/matplotlib/axes/_base.py", line 370, in _plot_args
x, y = self._xy_from_xy(x, y)
File "/usr/local/lib64/python3.7/site-packages/matplotlib/axes/_base.py", line 231, in _xy_from_xy
"have shapes {} and {}".format(x.shape, y.shape))
ValueError: x and y must have same first dimension, but have shapes (21,) and (1,)
event_date key count
7/23/23 0:00 389628-135052 74858
7/28/23 0:00 389628-135052 75139
7/12/23 0:00 389631-135055 60910
7/18/23 0:00 389632-135056 68850
7/26/23 0:00 389632-135056 33704
7/27/23 0:00 389630-135054 119679
7/20/23 0:00 389632-135056 71281
7/15/23 0:00 389632-135056 68854
7/23/23 0:00 389634-135058 69020
7/20/23 0:00 389629-135053 59536
7/21/23 0:00 389631-135055 71065
7/25/23 0:00 389629-135053 66887
7/15/23 0:00 389629-135053 66150
7/12/23 0:00 389633-135057 53096
7/14/23 0:00 389634-135058 62948
7/25/23 0:00 389628-135052 74872
7/15/23 0:00 389631-135055 73870
7/18/23 0:00 389631-135055 74548
7/17/23 0:00 389632-135056 68402
7/20/23 0:00 389633-135057 54665
7/15/23 0:00 389633-135057 64637
7/30/23 0:00 389630-135054 123113
7/21/23 0:00 389630-135054 67368
7/12/23 0:00 389632-135056 55618
7/19/23 0:00 389633-135057 70942
7/21/23 0:00 389633-135057 68221
7/18/23 0:00 389628-135052 76602
8/1/23 0:00 389631-135055 13252
7/17/23 0:00 389629-135053 64287
7/25/23 0:00 389634-135058 68104
7/17/23 0:00 389634-135058 66301
7/12/23 0:00 389628-135052 61841
7/18/23 0:00 389630-135054 71472
7/24/23 0:00 389629-135053 68495
7/30/23 0:00 389629-135053 122907
7/26/23 0:00 389630-135054 26650
7/30/23 0:00 389632-135056 134425
7/22/23 0:00 389634-135058 62225
7/18/23 0:00 389633-135057 61047
7/22/23 0:00 389633-135057 60926
7/18/23 0:00 389634-135058 67725
7/16/23 0:00 389633-135057 64254
7/14/23 0:00 389633-135057 61383
7/24/23 0:00 389633-135057 66471
7/16/23 0:00 389629-135053 66548
7/19/23 0:00 389628-135052 75846
7/17/23 0:00 389631-135055 73452
7/13/23 0:00 389631-135055 82725
7/31/23 0:00 389634-135058 41786
7/26/23 0:00 389629-135053 68862
8/1/23 0:00 389633-135057 12333
7/21/23 0:00 389628-135052 72381
7/30/23 0:00 389628-135052 77991
7/19/23 0:00 389630-135054 68765
8/1/23 0:00 389630-135054 12798
7/21/23 0:00 389632-135056 66499
7/29/23 0:00 389633-135057 16644
7/20/23 0:00 389631-135055 74593
7/24/23 0:00 389630-135054 72015
7/27/23 0:00 389632-135056 98245
7/31/23 0:00 389630-135054 56117
7/22/23 0:00 389629-135053 62669
7/23/23 0:00 389631-135055 74936
7/25/23 0:00 389632-135056 69935
7/29/23 0:00 389630-135054 23579
7/13/23 0:00 389632-135056 71917
7/13/23 0:00 389633-135057 67979
7/19/23 0:00 389631-135055 74154
7/23/23 0:00 389632-135056 71347
7/27/23 0:00 389634-135058 57570
7/26/23 0:00 389633-135057 60073
7/17/23 0:00 389633-135057 66860
7/15/23 0:00 389628-135052 75962
7/29/23 0:00 389628-135052 69251
7/27/23 0:00 389631-135055 69051
7/28/23 0:00 389631-135055 74231
7/23/23 0:00 389633-135057 66237
7/30/23 0:00 389634-135058 130063
7/25/23 0:00 389631-135055 73097
7/31/23 0:00 389628-135052 75428
7/24/23 0:00 389634-135058 69958
7/13/23 0:00 389634-135058 72563
7/27/23 0:00 389629-135053 63235
8/1/23 0:00 389629-135053 12444
7/26/23 0:00 389628-135052 80514
7/14/23 0:00 389628-135052 72334
7/30/23 0:00 389631-135055 130862
7/29/23 0:00 389634-135058 21234
7/14/23 0:00 389629-135053 62070
7/30/23 0:00 389633-135057 117653
7/17/23 0:00 389628-135052 74903
7/24/23 0:00 389628-135052 76074
7/28/23 0:00 389630-135054 58201
7/25/23 0:00 389630-135054 70594
7/21/23 0:00 389629-135053 70020
7/20/23 0:00 389634-135058 59776
7/22/23 0:00 389631-135055 73678
7/19/23 0:00 389632-135056 68493
7/28/23 0:00 389632-135056 69294
7/29/23 0:00 389632-135056 16416
8/1/23 0:00 389634-135058 12202
7/16/23 0:00 389628-135052 74739
7/13/23 0:00 389628-135052 78198
7/16/23 0:00 389630-135054 70980
7/31/23 0:00 389632-135056 50267
7/26/23 0:00 389634-135058 77612
7/31/23 0:00 389631-135055 45171
7/22/23 0:00 389630-135054 70867
7/15/23 0:00 389634-135058 67374
7/31/23 0:00 389633-135057 50583
7/19/23 0:00 389629-135053 72029
7/22/23 0:00 389628-135052 75503
7/14/23 0:00 389632-135056 65704
7/12/23 0:00 389634-135058 54886
7/21/23 0:00 389634-135058 70547
7/16/23 0:00 389634-135058 68285
7/27/23 0:00 389628-135052 68078
7/17/23 0:00 389630-135054 70450
7/31/23 0:00 389629-135053 50228
7/28/23 0:00 389629-135053 65580
7/13/23 0:00 389629-135053 69155
7/23/23 0:00 389630-135054 70885
7/14/23 0:00 389630-135054 67892
7/19/23 0:00 389634-135058 73446
7/27/23 0:00 389633-135057 60125
7/28/23 0:00 389633-135057 64199
7/29/23 0:00 389631-135055 33164
7/16/23 0:00 389631-135055 73831
7/24/23 0:00 389632-135056 70333
7/16/23 0:00 389632-135056 68069
7/18/23 0:00 389629-135053 67103
7/24/23 0:00 389631-135055 73852
7/20/23 0:00 389630-135054 72357
7/15/23 0:00 389630-135054 70673
7/12/23 0:00 389630-135054 57477
7/29/23 0:00 389629-135053 18198
7/14/23 0:00 389631-135055 63845
8/1/23 0:00 389632-135056 12744
7/28/23 0:00 389634-135058 67665
7/25/23 0:00 389633-135057 68534
7/12/23 0:00 389629-135053 53612
8/1/23 0:00 389628-135052 12663
7/20/23 0:00 389628-135052 75662
7/26/23 0:00 389631-135055 75014
7/22/23 0:00 389632-135056 68687
7/13/23 0:00 389630-135054 72816
7/23/23 0:00 389629-135053 68272
答：
1赞
Trenton McKinney
10/18/2023
#1
- 现有代码的问题是镜像
熊猫。Series.count方法。如果列名镜像方法，则不能使用表示法。.count.pandasdf[df.key == k]['count']不df[df.key == k].count
- 此外，使用仍会导致问题，如图所示。 还需要排序，并且相对于 。
plt.plotplt.plotxyx
- 一种选择是与 一起使用，或使用 。
sns.relplotkind='line'sns.lineplot
import seaborn as sns
g = sns.relplot(kind='line', data=df, x='event_date', y='count', hue='key', height=7.5, aspect=1)
- 要通过选择每个组来迭代创建绘图，请创建一个可重复的绘图。
figureAxes
fig, ax = plt.subplots(figsize=(10, 10))
for g in df.key.unique():
df[df.key.eq(g)].plot(x='event_date', y='count', ax=ax, label=g)
- 用于从长形式到宽形式进行重塑，并在没有循环的情况下进行绘图。
pandas.DataFrame.pivot - 如果每个组中的日期具有多个值，则用于聚合数据。
pandas.DataFrame.pivot_table
# pivot the dataframe into a wide format
dfp = df.pivot(index='event_date', columns='key', values='count')
# and then plot without a loop
ax = dfp.plot(figsize=(10, 10))
示例 DataFrame
import pandas as pd
data = {'event_date': [pd.Timestamp('2023-07-23 00:00:00'), pd.Timestamp('2023-07-28 00:00:00'), pd.Timestamp('2023-07-12 00:00:00'), pd.Timestamp('2023-07-18 00:00:00'), pd.Timestamp('2023-07-26 00:00:00'), pd.Timestamp('2023-07-27 00:00:00'), pd.Timestamp('2023-07-20 00:00:00'), pd.Timestamp('2023-07-15 00:00:00'), pd.Timestamp('2023-07-23 00:00:00'), pd.Timestamp('2023-07-20 00:00:00'), pd.Timestamp('2023-07-21 00:00:00'), pd.Timestamp('2023-07-25 00:00:00'), pd.Timestamp('2023-07-15 00:00:00'), pd.Timestamp('2023-07-12 00:00:00'), pd.Timestamp('2023-07-14 00:00:00'), pd.Timestamp('2023-07-25 00:00:00'), pd.Timestamp('2023-07-15 00:00:00'), pd.Timestamp('2023-07-18 00:00:00'), pd.Timestamp('2023-07-17 00:00:00'), pd.Timestamp('2023-07-20 00:00:00'), pd.Timestamp('2023-07-15 00:00:00'), pd.Timestamp('2023-07-30 00:00:00'), pd.Timestamp('2023-07-21 00:00:00'), pd.Timestamp('2023-07-12 00:00:00'), pd.Timestamp('2023-07-19 00:00:00'), pd.Timestamp('2023-07-21 00:00:00'), pd.Timestamp('2023-07-18 00:00:00'), pd.Timestamp('2023-08-01 00:00:00'), pd.Timestamp('2023-07-17 00:00:00'), pd.Timestamp('2023-07-25 00:00:00'), pd.Timestamp('2023-07-17 00:00:00'), pd.Timestamp('2023-07-12 00:00:00'), pd.Timestamp('2023-07-18 00:00:00'), pd.Timestamp('2023-07-24 00:00:00'), pd.Timestamp('2023-07-30 00:00:00'), pd.Timestamp('2023-07-26 00:00:00'), pd.Timestamp('2023-07-30 00:00:00'), pd.Timestamp('2023-07-22 00:00:00'), pd.Timestamp('2023-07-18 00:00:00'), pd.Timestamp('2023-07-22 00:00:00'), pd.Timestamp('2023-07-18 00:00:00'), pd.Timestamp('2023-07-16 00:00:00'), pd.Timestamp('2023-07-14 00:00:00'), pd.Timestamp('2023-07-24 00:00:00'), pd.Timestamp('2023-07-16 00:00:00'), pd.Timestamp('2023-07-19 00:00:00'), pd.Timestamp('2023-07-17 00:00:00'), pd.Timestamp('2023-07-13 00:00:00'), pd.Timestamp('2023-07-31 00:00:00'), pd.Timestamp('2023-07-26 00:00:00'), pd.Timestamp('2023-08-01 00:00:00'), pd.Timestamp('2023-07-21 00:00:00'), pd.Timestamp('2023-07-30 00:00:00'), pd.Timestamp('2023-07-19 00:00:00'), pd.Timestamp('2023-08-01 00:00:00'), pd.Timestamp('2023-07-21 00:00:00'), pd.Timestamp('2023-07-29 00:00:00'), pd.Timestamp('2023-07-20 00:00:00'), pd.Timestamp('2023-07-24 00:00:00'), pd.Timestamp('2023-07-27 00:00:00'), pd.Timestamp('2023-07-31 00:00:00'), pd.Timestamp('2023-07-22 00:00:00'), pd.Timestamp('2023-07-23 00:00:00'), pd.Timestamp('2023-07-25 00:00:00'), pd.Timestamp('2023-07-29 00:00:00'), pd.Timestamp('2023-07-13 00:00:00'), pd.Timestamp('2023-07-13 00:00:00'), pd.Timestamp('2023-07-19 00:00:00'), pd.Timestamp('2023-07-23 00:00:00'), pd.Timestamp('2023-07-27 00:00:00'), pd.Timestamp('2023-07-26 00:00:00'), pd.Timestamp('2023-07-17 00:00:00'), pd.Timestamp('2023-07-15 00:00:00'), pd.Timestamp('2023-07-29 00:00:00'), pd.Timestamp('2023-07-27 00:00:00'), pd.Timestamp('2023-07-28 00:00:00'), pd.Timestamp('2023-07-23 00:00:00'), pd.Timestamp('2023-07-30 00:00:00'), pd.Timestamp('2023-07-25 00:00:00'), pd.Timestamp('2023-07-31 00:00:00'), pd.Timestamp('2023-07-24 00:00:00'), pd.Timestamp('2023-07-13 00:00:00'), pd.Timestamp('2023-07-27 00:00:00'), pd.Timestamp('2023-08-01 00:00:00'), pd.Timestamp('2023-07-26 00:00:00'), pd.Timestamp('2023-07-14 00:00:00'), pd.Timestamp('2023-07-30 00:00:00'), pd.Timestamp('2023-07-29 00:00:00'), pd.Timestamp('2023-07-14 00:00:00'), pd.Timestamp('2023-07-30 00:00:00'), pd.Timestamp('2023-07-17 00:00:00'), pd.Timestamp('2023-07-24 00:00:00'), pd.Timestamp('2023-07-28 00:00:00'), pd.Timestamp('2023-07-25 00:00:00'), pd.Timestamp('2023-07-21 00:00:00'), pd.Timestamp('2023-07-20 00:00:00'), pd.Timestamp('2023-07-22 00:00:00'), pd.Timestamp('2023-07-19 00:00:00'), pd.Timestamp('2023-07-28 00:00:00'), pd.Timestamp('2023-07-29 00:00:00'), pd.Timestamp('2023-08-01 00:00:00'), pd.Timestamp('2023-07-16 00:00:00'), pd.Timestamp('2023-07-13 00:00:00'), pd.Timestamp('2023-07-16 00:00:00'), pd.Timestamp('2023-07-31 00:00:00'), pd.Timestamp('2023-07-26 00:00:00'), pd.Timestamp('2023-07-31 00:00:00'), pd.Timestamp('2023-07-22 00:00:00'), pd.Timestamp('2023-07-15 00:00:00'), pd.Timestamp('2023-07-31 00:00:00'), pd.Timestamp('2023-07-19 00:00:00'), pd.Timestamp('2023-07-22 00:00:00'), pd.Timestamp('2023-07-14 00:00:00'), pd.Timestamp('2023-07-12 00:00:00'), pd.Timestamp('2023-07-21 00:00:00'), pd.Timestamp('2023-07-16 00:00:00'), pd.Timestamp('2023-07-27 00:00:00'), pd.Timestamp('2023-07-17 00:00:00'), pd.Timestamp('2023-07-31 00:00:00'), pd.Timestamp('2023-07-28 00:00:00'), pd.Timestamp('2023-07-13 00:00:00'), pd.Timestamp('2023-07-23 00:00:00'), pd.Timestamp('2023-07-14 00:00:00'), pd.Timestamp('2023-07-19 00:00:00'), pd.Timestamp('2023-07-27 00:00:00'), pd.Timestamp('2023-07-28 00:00:00'), pd.Timestamp('2023-07-29 00:00:00'), pd.Timestamp('2023-07-16 00:00:00'), pd.Timestamp('2023-07-24 00:00:00'), pd.Timestamp('2023-07-16 00:00:00'), pd.Timestamp('2023-07-18 00:00:00'), pd.Timestamp('2023-07-24 00:00:00'), pd.Timestamp('2023-07-20 00:00:00'), pd.Timestamp('2023-07-15 00:00:00'), pd.Timestamp('2023-07-12 00:00:00'), pd.Timestamp('2023-07-29 00:00:00'), pd.Timestamp('2023-07-14 00:00:00'), pd.Timestamp('2023-08-01 00:00:00'), pd.Timestamp('2023-07-28 00:00:00'), pd.Timestamp('2023-07-25 00:00:00'), pd.Timestamp('2023-07-12 00:00:00'), pd.Timestamp('2023-08-01 00:00:00'), pd.Timestamp('2023-07-20 00:00:00'), pd.Timestamp('2023-07-26 00:00:00'), pd.Timestamp('2023-07-22 00:00:00'), pd.Timestamp('2023-07-13 00:00:00'), pd.Timestamp('2023-07-23 00:00:00')],
'key': ['389628-135052', '389628-135052', '389631-135055', '389632-135056', '389632-135056', '389630-135054', '389632-135056', '389632-135056', '389634-135058', '389629-135053', '389631-135055', '389629-135053', '389629-135053', '389633-135057', '389634-135058', '389628-135052', '389631-135055', '389631-135055', '389632-135056', '389633-135057', '389633-135057', '389630-135054', '389630-135054', '389632-135056', '389633-135057', '389633-135057', '389628-135052', '389631-135055', '389629-135053', '389634-135058', '389634-135058', '389628-135052', '389630-135054', '389629-135053', '389629-135053', '389630-135054', '389632-135056', '389634-135058', '389633-135057', '389633-135057', '389634-135058', '389633-135057', '389633-135057', '389633-135057', '389629-135053', '389628-135052', '389631-135055', '389631-135055', '389634-135058', '389629-135053', '389633-135057', '389628-135052', '389628-135052', '389630-135054', '389630-135054', '389632-135056', '389633-135057', '389631-135055', '389630-135054', '389632-135056', '389630-135054', '389629-135053', '389631-135055', '389632-135056', '389630-135054', '389632-135056', '389633-135057', '389631-135055', '389632-135056', '389634-135058', '389633-135057', '389633-135057', '389628-135052', '389628-135052', '389631-135055', '389631-135055', '389633-135057', '389634-135058', '389631-135055', '389628-135052', '389634-135058', '389634-135058', '389629-135053', '389629-135053', '389628-135052', '389628-135052', '389631-135055', '389634-135058', '389629-135053', '389633-135057', '389628-135052', '389628-135052', '389630-135054', '389630-135054', '389629-135053', '389634-135058', '389631-135055', '389632-135056', '389632-135056', '389632-135056', '389634-135058', '389628-135052', '389628-135052', '389630-135054', '389632-135056', '389634-135058', '389631-135055', '389630-135054', '389634-135058', '389633-135057', '389629-135053', '389628-135052', '389632-135056', '389634-135058', '389634-135058', '389634-135058', '389628-135052', '389630-135054', '389629-135053', '389629-135053', '389629-135053', '389630-135054', '389630-135054', '389634-135058', '389633-135057', '389633-135057', '389631-135055', '389631-135055', '389632-135056', '389632-135056', '389629-135053', '389631-135055', '389630-135054', '389630-135054', '389630-135054', '389629-135053', '389631-135055', '389632-135056', '389634-135058', '389633-135057', '389629-135053', '389628-135052', '389628-135052', '389631-135055', '389632-135056', '389630-135054', '389629-135053'],
'count': [74858, 75139, 60910, 68850, 33704, 119679, 71281, 68854, 69020, 59536, 71065, 66887, 66150, 53096, 62948, 74872, 73870, 74548, 68402, 54665, 64637, 123113, 67368, 55618, 70942, 68221, 76602, 13252, 64287, 68104, 66301, 61841, 71472, 68495, 122907, 26650, 134425, 62225, 61047, 60926, 67725, 64254, 61383, 66471, 66548, 75846, 73452, 82725, 41786, 68862, 12333, 72381, 77991, 68765, 12798, 66499, 16644, 74593, 72015, 98245, 56117, 62669, 74936, 69935, 23579, 71917, 67979, 74154, 71347, 57570, 60073, 66860, 75962, 69251, 69051, 74231, 66237, 130063, 73097, 75428, 69958, 72563, 63235, 12444, 80514, 72334, 130862, 21234, 62070, 117653, 74903, 76074, 58201, 70594, 70020, 59776, 73678, 68493, 69294, 16416, 12202, 74739, 78198, 70980, 50267, 77612, 45171, 70867, 67374, 50583, 72029, 75503, 65704, 54886, 70547, 68285, 68078, 70450, 50228, 65580, 69155, 70885, 67892, 73446, 60125, 64199, 33164, 73831, 70333, 68069, 67103, 73852, 72357, 70673, 57477, 18198, 63845, 12744, 67665, 68534, 53612, 12663, 75662, 75014, 68687, 72816, 68272]}
df = pd.DataFrame(data)
dfp
key 389628-135052 389629-135053 389630-135054 389631-135055 389632-135056 389633-135057 389634-135058
event_date
2023-07-12 61841 53612 57477 60910 55618 53096 54886
2023-07-13 78198 69155 72816 82725 71917 67979 72563
2023-07-14 72334 62070 67892 63845 65704 61383 62948
2023-07-15 75962 66150 70673 73870 68854 64637 67374
2023-07-16 74739 66548 70980 73831 68069 64254 68285
2023-07-17 74903 64287 70450 73452 68402 66860 66301
2023-07-18 76602 67103 71472 74548 68850 61047 67725
2023-07-19 75846 72029 68765 74154 68493 70942 73446
2023-07-20 75662 59536 72357 74593 71281 54665 59776
2023-07-21 72381 70020 67368 71065 66499 68221 70547
2023-07-22 75503 62669 70867 73678 68687 60926 62225
2023-07-23 74858 68272 70885 74936 71347 66237 69020
2023-07-24 76074 68495 72015 73852 70333 66471 69958
2023-07-25 74872 66887 70594 73097 69935 68534 68104
2023-07-26 80514 68862 26650 75014 33704 60073 77612
2023-07-27 68078 63235 119679 69051 98245 60125 57570
2023-07-28 75139 65580 58201 74231 69294 64199 67665
2023-07-29 69251 18198 23579 33164 16416 16644 21234
2023-07-30 77991 122907 123113 130862 134425 117653 130063
2023-07-31 75428 50228 56117 45171 50267 50583 41786
2023-08-01 12663 12444 12798 13252 12744 12333 12202
评论
1赞
Trenton McKinney
10/18/2023
@Explorer已更新
1赞
Trenton McKinney
10/18/2023
@Explorer这是一个微妙的错误/问题。另请注意，添加了第三个没有循环的绘图选项
0赞
Trenton McKinney
10/19/2023
@Explorer，如果复制我的示例数据帧，我发布的代码是否有效或导致错误。
0赞
Explorer
10/19/2023
让我们在聊天中继续讨论。
评论