Question: I am trying to plot a timeseries graph for all the unique keys in my dataset, below is my dataset, I have 7 unique keys and I am trying to plot event_date on x-axis and line graph for count on y axis. I am looping through each unique key and trying to plot date vs count however getting below error. Based on the error I am not able to understand why shape for y is (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 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Answer:  The issue with the existing code is .count mirrors the pandas.Series.count method. You may not use . notation if the column name mirrors a pandas method.  df[df.key == k]['count'] not df[df.key == k].count   Additionally, using plt.plot will still cause issues, as seen in this plot. plt.plot also requires sorting x, and y relative to x.    One option is to use sns.relplot with kind='line', or use 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)    To iteratively create the plot by selecting each group, create a figure and Axes to repeatable plot each group onto.  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)    Use pandas.DataFrame.pivot to reshape from a long to wide form and plot without a loop. Use pandas.DataFrame.pivot_table to aggregate data if the dates in each group have more than one value.  # 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))   Sample 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