Weird behavior of barplot from python matplotlib with datetime












0















import matplotlib.pyplot as plt
import datetime
x = [datetime.datetime(1943,3, 13,12,0,0),
datetime.datetime(1943,3, 13,12,5,0),
datetime.datetime(1943,3, 13,12,10,0),
datetime.datetime(1943,3, 13,12,15,0),
datetime.datetime(1943,3, 13,12,20,0),
datetime.datetime(1943,3, 13,12,25,0),
datetime.datetime(1943,3, 13,12,30,0),
datetime.datetime(1943,3, 13,12,35,0)]
y = [1,2,3,4,2,1,3,4]

# plot the data out but does not provide sufficient detail on the lower values
plt.figure()
plt.bar(x,y)

# plot the data out but ommit the datetime information
plt.figure()
plt.bar(range(0,len(x)),y)


Hello guys, I am just starting with the matplotlib in transition from matlab to python. However, I encountered weird behavior of matplotlib as it is not able to display the data along with the datetime element.
My question here would be the output of both bar plot yield two different results.



enter image description here



The first one directly convert the data into some kind of continuous data where as the second one more like categorical data. Do anyone encountered similar problem as mine and dont mind share their way of approaching this?



P/s: i tried seaborn and it works but somehow does not play well with dual axis plotting. I also googled for similar issue but somehow not such issue?










share|improve this question





























    0















    import matplotlib.pyplot as plt
    import datetime
    x = [datetime.datetime(1943,3, 13,12,0,0),
    datetime.datetime(1943,3, 13,12,5,0),
    datetime.datetime(1943,3, 13,12,10,0),
    datetime.datetime(1943,3, 13,12,15,0),
    datetime.datetime(1943,3, 13,12,20,0),
    datetime.datetime(1943,3, 13,12,25,0),
    datetime.datetime(1943,3, 13,12,30,0),
    datetime.datetime(1943,3, 13,12,35,0)]
    y = [1,2,3,4,2,1,3,4]

    # plot the data out but does not provide sufficient detail on the lower values
    plt.figure()
    plt.bar(x,y)

    # plot the data out but ommit the datetime information
    plt.figure()
    plt.bar(range(0,len(x)),y)


    Hello guys, I am just starting with the matplotlib in transition from matlab to python. However, I encountered weird behavior of matplotlib as it is not able to display the data along with the datetime element.
    My question here would be the output of both bar plot yield two different results.



    enter image description here



    The first one directly convert the data into some kind of continuous data where as the second one more like categorical data. Do anyone encountered similar problem as mine and dont mind share their way of approaching this?



    P/s: i tried seaborn and it works but somehow does not play well with dual axis plotting. I also googled for similar issue but somehow not such issue?










    share|improve this question



























      0












      0








      0








      import matplotlib.pyplot as plt
      import datetime
      x = [datetime.datetime(1943,3, 13,12,0,0),
      datetime.datetime(1943,3, 13,12,5,0),
      datetime.datetime(1943,3, 13,12,10,0),
      datetime.datetime(1943,3, 13,12,15,0),
      datetime.datetime(1943,3, 13,12,20,0),
      datetime.datetime(1943,3, 13,12,25,0),
      datetime.datetime(1943,3, 13,12,30,0),
      datetime.datetime(1943,3, 13,12,35,0)]
      y = [1,2,3,4,2,1,3,4]

      # plot the data out but does not provide sufficient detail on the lower values
      plt.figure()
      plt.bar(x,y)

      # plot the data out but ommit the datetime information
      plt.figure()
      plt.bar(range(0,len(x)),y)


      Hello guys, I am just starting with the matplotlib in transition from matlab to python. However, I encountered weird behavior of matplotlib as it is not able to display the data along with the datetime element.
      My question here would be the output of both bar plot yield two different results.



      enter image description here



      The first one directly convert the data into some kind of continuous data where as the second one more like categorical data. Do anyone encountered similar problem as mine and dont mind share their way of approaching this?



      P/s: i tried seaborn and it works but somehow does not play well with dual axis plotting. I also googled for similar issue but somehow not such issue?










      share|improve this question
















      import matplotlib.pyplot as plt
      import datetime
      x = [datetime.datetime(1943,3, 13,12,0,0),
      datetime.datetime(1943,3, 13,12,5,0),
      datetime.datetime(1943,3, 13,12,10,0),
      datetime.datetime(1943,3, 13,12,15,0),
      datetime.datetime(1943,3, 13,12,20,0),
      datetime.datetime(1943,3, 13,12,25,0),
      datetime.datetime(1943,3, 13,12,30,0),
      datetime.datetime(1943,3, 13,12,35,0)]
      y = [1,2,3,4,2,1,3,4]

      # plot the data out but does not provide sufficient detail on the lower values
      plt.figure()
      plt.bar(x,y)

      # plot the data out but ommit the datetime information
      plt.figure()
      plt.bar(range(0,len(x)),y)


      Hello guys, I am just starting with the matplotlib in transition from matlab to python. However, I encountered weird behavior of matplotlib as it is not able to display the data along with the datetime element.
      My question here would be the output of both bar plot yield two different results.



      enter image description here



      The first one directly convert the data into some kind of continuous data where as the second one more like categorical data. Do anyone encountered similar problem as mine and dont mind share their way of approaching this?



      P/s: i tried seaborn and it works but somehow does not play well with dual axis plotting. I also googled for similar issue but somehow not such issue?







      python datetime matplotlib bar-chart visualize






      share|improve this question















      share|improve this question













      share|improve this question




      share|improve this question








      edited Nov 21 '18 at 3:38









      CozyAzure

      5,41841836




      5,41841836










      asked Nov 21 '18 at 3:31









      Billy LauBilly Lau

      31




      31
























          2 Answers
          2






          active

          oldest

          votes


















          1














          I'm not sure if I would call the observed behaviour unexpected. In the first case you provide dates to the x variable of the bar plot, hence it will plot the bars at those dates. In the second case you provide some numbers to the x variable, hence it will plot the numbers.



          Since you didn't tell which of those you actually prefer, a solution is to make them both equal visually. Still, the respective concept is different.



          import matplotlib.pyplot as plt
          from matplotlib.dates import DateFormatter
          import datetime
          x = [datetime.datetime(1943,3, 13,12,0,0),
          datetime.datetime(1943,3, 13,12,5,0),
          datetime.datetime(1943,3, 13,12,10,0),
          datetime.datetime(1943,3, 13,12,15,0),
          datetime.datetime(1943,3, 13,12,20,0),
          datetime.datetime(1943,3, 13,12,25,0),
          datetime.datetime(1943,3, 13,12,30,0),
          datetime.datetime(1943,3, 13,12,35,0)]
          y = [1,2,3,4,2,1,3,4]

          # plot numeric plot
          plt.figure()
          plt.bar(x,y, width=4./24/60) # 4 minutes wide bars
          plt.gca().xaxis.set_major_formatter(DateFormatter("%H:%M"))

          # Plot categorical plot
          plt.figure()
          plt.bar(range(0,len(x)),y, width=0.8) # 0.8 units wide bars
          plt.xticks(range(0,len(x)), [d.strftime("%H:%M") for d in x])

          plt.show()


          enter image description here



          The difference between the concepts would however be more clearly observable when using different data,



          x = [datetime.datetime(1943,3, 13,12,0,0),
          datetime.datetime(1943,3, 13,12,5,0),
          datetime.datetime(1943,3, 13,12,15,0),
          datetime.datetime(1943,3, 13,12,25,0),
          datetime.datetime(1943,3, 13,12,30,0),
          datetime.datetime(1943,3, 13,12,35,0),
          datetime.datetime(1943,3, 13,12,45,0),
          datetime.datetime(1943,3, 13,12,50,0)]


          enter image description here






          share|improve this answer


























          • A small addition for the OP, the formats accepted by DateFormatter can be found here (docs.python.org/3/library/…).

            – Patol75
            Nov 21 '18 at 4:34











          • I think the relation between matlab datetime formats ('HH:MM') and python ('%H:%M') is pretty obvious in this case, but thanks for then link anyways.

            – ImportanceOfBeingErnest
            Nov 21 '18 at 4:41











          • Thanks. I guess the keyword here would be the "width" of datetime. I did not realize it plays a part in determining whether the bar is continuous or discrete. I guess i do get a little bit confused about the datetime format in python. Thanks again for pointing out!

            – Billy Lau
            Nov 21 '18 at 4:46













          • The width does not play a part in determining the axis units. It's really x itself that determines the units. If those are datetimes, the units are different than if they are integers starting at 0.

            – ImportanceOfBeingErnest
            Nov 21 '18 at 5:00











          • i see. Thanks for clarifying!

            – Billy Lau
            Nov 21 '18 at 5:42



















          0














          I'm not sure about how to fix the problems with matplotlib and datetime, but pandas handles datetime objects very well. You can consider it. You can do, for example, the following:



          import pandas as pd
          df = pd.DataFrame({'date': x, 'value': y})
          df.set_index('date').plot.bar()
          plt.show()


          pandas result



          And improvements are pretty easy to do too:



          df = pd.DataFrame({'date': x, 'value': y})
          df['date'] = df['date'].dt.time
          df.set_index('date').plot.bar(rot=0, figsize=(10, 5), alpha=0.7)
          plt.show()


          Image 2






          share|improve this answer
























          • "but pandas handles datetime objects very well" - Note that pandas in this case does not use datetime objects as real dates. You would see that if using unequal spacings between the dates. They would still be equally spaced on the axis.

            – ImportanceOfBeingErnest
            Nov 21 '18 at 4:06











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          2 Answers
          2






          active

          oldest

          votes








          2 Answers
          2






          active

          oldest

          votes









          active

          oldest

          votes






          active

          oldest

          votes









          1














          I'm not sure if I would call the observed behaviour unexpected. In the first case you provide dates to the x variable of the bar plot, hence it will plot the bars at those dates. In the second case you provide some numbers to the x variable, hence it will plot the numbers.



          Since you didn't tell which of those you actually prefer, a solution is to make them both equal visually. Still, the respective concept is different.



          import matplotlib.pyplot as plt
          from matplotlib.dates import DateFormatter
          import datetime
          x = [datetime.datetime(1943,3, 13,12,0,0),
          datetime.datetime(1943,3, 13,12,5,0),
          datetime.datetime(1943,3, 13,12,10,0),
          datetime.datetime(1943,3, 13,12,15,0),
          datetime.datetime(1943,3, 13,12,20,0),
          datetime.datetime(1943,3, 13,12,25,0),
          datetime.datetime(1943,3, 13,12,30,0),
          datetime.datetime(1943,3, 13,12,35,0)]
          y = [1,2,3,4,2,1,3,4]

          # plot numeric plot
          plt.figure()
          plt.bar(x,y, width=4./24/60) # 4 minutes wide bars
          plt.gca().xaxis.set_major_formatter(DateFormatter("%H:%M"))

          # Plot categorical plot
          plt.figure()
          plt.bar(range(0,len(x)),y, width=0.8) # 0.8 units wide bars
          plt.xticks(range(0,len(x)), [d.strftime("%H:%M") for d in x])

          plt.show()


          enter image description here



          The difference between the concepts would however be more clearly observable when using different data,



          x = [datetime.datetime(1943,3, 13,12,0,0),
          datetime.datetime(1943,3, 13,12,5,0),
          datetime.datetime(1943,3, 13,12,15,0),
          datetime.datetime(1943,3, 13,12,25,0),
          datetime.datetime(1943,3, 13,12,30,0),
          datetime.datetime(1943,3, 13,12,35,0),
          datetime.datetime(1943,3, 13,12,45,0),
          datetime.datetime(1943,3, 13,12,50,0)]


          enter image description here






          share|improve this answer


























          • A small addition for the OP, the formats accepted by DateFormatter can be found here (docs.python.org/3/library/…).

            – Patol75
            Nov 21 '18 at 4:34











          • I think the relation between matlab datetime formats ('HH:MM') and python ('%H:%M') is pretty obvious in this case, but thanks for then link anyways.

            – ImportanceOfBeingErnest
            Nov 21 '18 at 4:41











          • Thanks. I guess the keyword here would be the "width" of datetime. I did not realize it plays a part in determining whether the bar is continuous or discrete. I guess i do get a little bit confused about the datetime format in python. Thanks again for pointing out!

            – Billy Lau
            Nov 21 '18 at 4:46













          • The width does not play a part in determining the axis units. It's really x itself that determines the units. If those are datetimes, the units are different than if they are integers starting at 0.

            – ImportanceOfBeingErnest
            Nov 21 '18 at 5:00











          • i see. Thanks for clarifying!

            – Billy Lau
            Nov 21 '18 at 5:42
















          1














          I'm not sure if I would call the observed behaviour unexpected. In the first case you provide dates to the x variable of the bar plot, hence it will plot the bars at those dates. In the second case you provide some numbers to the x variable, hence it will plot the numbers.



          Since you didn't tell which of those you actually prefer, a solution is to make them both equal visually. Still, the respective concept is different.



          import matplotlib.pyplot as plt
          from matplotlib.dates import DateFormatter
          import datetime
          x = [datetime.datetime(1943,3, 13,12,0,0),
          datetime.datetime(1943,3, 13,12,5,0),
          datetime.datetime(1943,3, 13,12,10,0),
          datetime.datetime(1943,3, 13,12,15,0),
          datetime.datetime(1943,3, 13,12,20,0),
          datetime.datetime(1943,3, 13,12,25,0),
          datetime.datetime(1943,3, 13,12,30,0),
          datetime.datetime(1943,3, 13,12,35,0)]
          y = [1,2,3,4,2,1,3,4]

          # plot numeric plot
          plt.figure()
          plt.bar(x,y, width=4./24/60) # 4 minutes wide bars
          plt.gca().xaxis.set_major_formatter(DateFormatter("%H:%M"))

          # Plot categorical plot
          plt.figure()
          plt.bar(range(0,len(x)),y, width=0.8) # 0.8 units wide bars
          plt.xticks(range(0,len(x)), [d.strftime("%H:%M") for d in x])

          plt.show()


          enter image description here



          The difference between the concepts would however be more clearly observable when using different data,



          x = [datetime.datetime(1943,3, 13,12,0,0),
          datetime.datetime(1943,3, 13,12,5,0),
          datetime.datetime(1943,3, 13,12,15,0),
          datetime.datetime(1943,3, 13,12,25,0),
          datetime.datetime(1943,3, 13,12,30,0),
          datetime.datetime(1943,3, 13,12,35,0),
          datetime.datetime(1943,3, 13,12,45,0),
          datetime.datetime(1943,3, 13,12,50,0)]


          enter image description here






          share|improve this answer


























          • A small addition for the OP, the formats accepted by DateFormatter can be found here (docs.python.org/3/library/…).

            – Patol75
            Nov 21 '18 at 4:34











          • I think the relation between matlab datetime formats ('HH:MM') and python ('%H:%M') is pretty obvious in this case, but thanks for then link anyways.

            – ImportanceOfBeingErnest
            Nov 21 '18 at 4:41











          • Thanks. I guess the keyword here would be the "width" of datetime. I did not realize it plays a part in determining whether the bar is continuous or discrete. I guess i do get a little bit confused about the datetime format in python. Thanks again for pointing out!

            – Billy Lau
            Nov 21 '18 at 4:46













          • The width does not play a part in determining the axis units. It's really x itself that determines the units. If those are datetimes, the units are different than if they are integers starting at 0.

            – ImportanceOfBeingErnest
            Nov 21 '18 at 5:00











          • i see. Thanks for clarifying!

            – Billy Lau
            Nov 21 '18 at 5:42














          1












          1








          1







          I'm not sure if I would call the observed behaviour unexpected. In the first case you provide dates to the x variable of the bar plot, hence it will plot the bars at those dates. In the second case you provide some numbers to the x variable, hence it will plot the numbers.



          Since you didn't tell which of those you actually prefer, a solution is to make them both equal visually. Still, the respective concept is different.



          import matplotlib.pyplot as plt
          from matplotlib.dates import DateFormatter
          import datetime
          x = [datetime.datetime(1943,3, 13,12,0,0),
          datetime.datetime(1943,3, 13,12,5,0),
          datetime.datetime(1943,3, 13,12,10,0),
          datetime.datetime(1943,3, 13,12,15,0),
          datetime.datetime(1943,3, 13,12,20,0),
          datetime.datetime(1943,3, 13,12,25,0),
          datetime.datetime(1943,3, 13,12,30,0),
          datetime.datetime(1943,3, 13,12,35,0)]
          y = [1,2,3,4,2,1,3,4]

          # plot numeric plot
          plt.figure()
          plt.bar(x,y, width=4./24/60) # 4 minutes wide bars
          plt.gca().xaxis.set_major_formatter(DateFormatter("%H:%M"))

          # Plot categorical plot
          plt.figure()
          plt.bar(range(0,len(x)),y, width=0.8) # 0.8 units wide bars
          plt.xticks(range(0,len(x)), [d.strftime("%H:%M") for d in x])

          plt.show()


          enter image description here



          The difference between the concepts would however be more clearly observable when using different data,



          x = [datetime.datetime(1943,3, 13,12,0,0),
          datetime.datetime(1943,3, 13,12,5,0),
          datetime.datetime(1943,3, 13,12,15,0),
          datetime.datetime(1943,3, 13,12,25,0),
          datetime.datetime(1943,3, 13,12,30,0),
          datetime.datetime(1943,3, 13,12,35,0),
          datetime.datetime(1943,3, 13,12,45,0),
          datetime.datetime(1943,3, 13,12,50,0)]


          enter image description here






          share|improve this answer















          I'm not sure if I would call the observed behaviour unexpected. In the first case you provide dates to the x variable of the bar plot, hence it will plot the bars at those dates. In the second case you provide some numbers to the x variable, hence it will plot the numbers.



          Since you didn't tell which of those you actually prefer, a solution is to make them both equal visually. Still, the respective concept is different.



          import matplotlib.pyplot as plt
          from matplotlib.dates import DateFormatter
          import datetime
          x = [datetime.datetime(1943,3, 13,12,0,0),
          datetime.datetime(1943,3, 13,12,5,0),
          datetime.datetime(1943,3, 13,12,10,0),
          datetime.datetime(1943,3, 13,12,15,0),
          datetime.datetime(1943,3, 13,12,20,0),
          datetime.datetime(1943,3, 13,12,25,0),
          datetime.datetime(1943,3, 13,12,30,0),
          datetime.datetime(1943,3, 13,12,35,0)]
          y = [1,2,3,4,2,1,3,4]

          # plot numeric plot
          plt.figure()
          plt.bar(x,y, width=4./24/60) # 4 minutes wide bars
          plt.gca().xaxis.set_major_formatter(DateFormatter("%H:%M"))

          # Plot categorical plot
          plt.figure()
          plt.bar(range(0,len(x)),y, width=0.8) # 0.8 units wide bars
          plt.xticks(range(0,len(x)), [d.strftime("%H:%M") for d in x])

          plt.show()


          enter image description here



          The difference between the concepts would however be more clearly observable when using different data,



          x = [datetime.datetime(1943,3, 13,12,0,0),
          datetime.datetime(1943,3, 13,12,5,0),
          datetime.datetime(1943,3, 13,12,15,0),
          datetime.datetime(1943,3, 13,12,25,0),
          datetime.datetime(1943,3, 13,12,30,0),
          datetime.datetime(1943,3, 13,12,35,0),
          datetime.datetime(1943,3, 13,12,45,0),
          datetime.datetime(1943,3, 13,12,50,0)]


          enter image description here







          share|improve this answer














          share|improve this answer



          share|improve this answer








          edited Nov 21 '18 at 4:26

























          answered Nov 21 '18 at 4:21









          ImportanceOfBeingErnestImportanceOfBeingErnest

          135k13151228




          135k13151228













          • A small addition for the OP, the formats accepted by DateFormatter can be found here (docs.python.org/3/library/…).

            – Patol75
            Nov 21 '18 at 4:34











          • I think the relation between matlab datetime formats ('HH:MM') and python ('%H:%M') is pretty obvious in this case, but thanks for then link anyways.

            – ImportanceOfBeingErnest
            Nov 21 '18 at 4:41











          • Thanks. I guess the keyword here would be the "width" of datetime. I did not realize it plays a part in determining whether the bar is continuous or discrete. I guess i do get a little bit confused about the datetime format in python. Thanks again for pointing out!

            – Billy Lau
            Nov 21 '18 at 4:46













          • The width does not play a part in determining the axis units. It's really x itself that determines the units. If those are datetimes, the units are different than if they are integers starting at 0.

            – ImportanceOfBeingErnest
            Nov 21 '18 at 5:00











          • i see. Thanks for clarifying!

            – Billy Lau
            Nov 21 '18 at 5:42



















          • A small addition for the OP, the formats accepted by DateFormatter can be found here (docs.python.org/3/library/…).

            – Patol75
            Nov 21 '18 at 4:34











          • I think the relation between matlab datetime formats ('HH:MM') and python ('%H:%M') is pretty obvious in this case, but thanks for then link anyways.

            – ImportanceOfBeingErnest
            Nov 21 '18 at 4:41











          • Thanks. I guess the keyword here would be the "width" of datetime. I did not realize it plays a part in determining whether the bar is continuous or discrete. I guess i do get a little bit confused about the datetime format in python. Thanks again for pointing out!

            – Billy Lau
            Nov 21 '18 at 4:46













          • The width does not play a part in determining the axis units. It's really x itself that determines the units. If those are datetimes, the units are different than if they are integers starting at 0.

            – ImportanceOfBeingErnest
            Nov 21 '18 at 5:00











          • i see. Thanks for clarifying!

            – Billy Lau
            Nov 21 '18 at 5:42

















          A small addition for the OP, the formats accepted by DateFormatter can be found here (docs.python.org/3/library/…).

          – Patol75
          Nov 21 '18 at 4:34





          A small addition for the OP, the formats accepted by DateFormatter can be found here (docs.python.org/3/library/…).

          – Patol75
          Nov 21 '18 at 4:34













          I think the relation between matlab datetime formats ('HH:MM') and python ('%H:%M') is pretty obvious in this case, but thanks for then link anyways.

          – ImportanceOfBeingErnest
          Nov 21 '18 at 4:41





          I think the relation between matlab datetime formats ('HH:MM') and python ('%H:%M') is pretty obvious in this case, but thanks for then link anyways.

          – ImportanceOfBeingErnest
          Nov 21 '18 at 4:41













          Thanks. I guess the keyword here would be the "width" of datetime. I did not realize it plays a part in determining whether the bar is continuous or discrete. I guess i do get a little bit confused about the datetime format in python. Thanks again for pointing out!

          – Billy Lau
          Nov 21 '18 at 4:46







          Thanks. I guess the keyword here would be the "width" of datetime. I did not realize it plays a part in determining whether the bar is continuous or discrete. I guess i do get a little bit confused about the datetime format in python. Thanks again for pointing out!

          – Billy Lau
          Nov 21 '18 at 4:46















          The width does not play a part in determining the axis units. It's really x itself that determines the units. If those are datetimes, the units are different than if they are integers starting at 0.

          – ImportanceOfBeingErnest
          Nov 21 '18 at 5:00





          The width does not play a part in determining the axis units. It's really x itself that determines the units. If those are datetimes, the units are different than if they are integers starting at 0.

          – ImportanceOfBeingErnest
          Nov 21 '18 at 5:00













          i see. Thanks for clarifying!

          – Billy Lau
          Nov 21 '18 at 5:42





          i see. Thanks for clarifying!

          – Billy Lau
          Nov 21 '18 at 5:42













          0














          I'm not sure about how to fix the problems with matplotlib and datetime, but pandas handles datetime objects very well. You can consider it. You can do, for example, the following:



          import pandas as pd
          df = pd.DataFrame({'date': x, 'value': y})
          df.set_index('date').plot.bar()
          plt.show()


          pandas result



          And improvements are pretty easy to do too:



          df = pd.DataFrame({'date': x, 'value': y})
          df['date'] = df['date'].dt.time
          df.set_index('date').plot.bar(rot=0, figsize=(10, 5), alpha=0.7)
          plt.show()


          Image 2






          share|improve this answer
























          • "but pandas handles datetime objects very well" - Note that pandas in this case does not use datetime objects as real dates. You would see that if using unequal spacings between the dates. They would still be equally spaced on the axis.

            – ImportanceOfBeingErnest
            Nov 21 '18 at 4:06
















          0














          I'm not sure about how to fix the problems with matplotlib and datetime, but pandas handles datetime objects very well. You can consider it. You can do, for example, the following:



          import pandas as pd
          df = pd.DataFrame({'date': x, 'value': y})
          df.set_index('date').plot.bar()
          plt.show()


          pandas result



          And improvements are pretty easy to do too:



          df = pd.DataFrame({'date': x, 'value': y})
          df['date'] = df['date'].dt.time
          df.set_index('date').plot.bar(rot=0, figsize=(10, 5), alpha=0.7)
          plt.show()


          Image 2






          share|improve this answer
























          • "but pandas handles datetime objects very well" - Note that pandas in this case does not use datetime objects as real dates. You would see that if using unequal spacings between the dates. They would still be equally spaced on the axis.

            – ImportanceOfBeingErnest
            Nov 21 '18 at 4:06














          0












          0








          0







          I'm not sure about how to fix the problems with matplotlib and datetime, but pandas handles datetime objects very well. You can consider it. You can do, for example, the following:



          import pandas as pd
          df = pd.DataFrame({'date': x, 'value': y})
          df.set_index('date').plot.bar()
          plt.show()


          pandas result



          And improvements are pretty easy to do too:



          df = pd.DataFrame({'date': x, 'value': y})
          df['date'] = df['date'].dt.time
          df.set_index('date').plot.bar(rot=0, figsize=(10, 5), alpha=0.7)
          plt.show()


          Image 2






          share|improve this answer













          I'm not sure about how to fix the problems with matplotlib and datetime, but pandas handles datetime objects very well. You can consider it. You can do, for example, the following:



          import pandas as pd
          df = pd.DataFrame({'date': x, 'value': y})
          df.set_index('date').plot.bar()
          plt.show()


          pandas result



          And improvements are pretty easy to do too:



          df = pd.DataFrame({'date': x, 'value': y})
          df['date'] = df['date'].dt.time
          df.set_index('date').plot.bar(rot=0, figsize=(10, 5), alpha=0.7)
          plt.show()


          Image 2







          share|improve this answer












          share|improve this answer



          share|improve this answer










          answered Nov 21 '18 at 3:44









          Julian PellerJulian Peller

          9041511




          9041511













          • "but pandas handles datetime objects very well" - Note that pandas in this case does not use datetime objects as real dates. You would see that if using unequal spacings between the dates. They would still be equally spaced on the axis.

            – ImportanceOfBeingErnest
            Nov 21 '18 at 4:06



















          • "but pandas handles datetime objects very well" - Note that pandas in this case does not use datetime objects as real dates. You would see that if using unequal spacings between the dates. They would still be equally spaced on the axis.

            – ImportanceOfBeingErnest
            Nov 21 '18 at 4:06

















          "but pandas handles datetime objects very well" - Note that pandas in this case does not use datetime objects as real dates. You would see that if using unequal spacings between the dates. They would still be equally spaced on the axis.

          – ImportanceOfBeingErnest
          Nov 21 '18 at 4:06





          "but pandas handles datetime objects very well" - Note that pandas in this case does not use datetime objects as real dates. You would see that if using unequal spacings between the dates. They would still be equally spaced on the axis.

          – ImportanceOfBeingErnest
          Nov 21 '18 at 4:06


















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