Plot two histograms on single chart with matplotlib

Question

I created a histogram plot using data from a file and no problem  Now I wanted to superpose data from another file in the same histogram  so I do something like this  n bins patchs   ax hist mydata1 100  n bins patchs   ax hist mydata2 100    but the problem is that for each interval  only the bar with the highest value appears  and the other is hidden  I wonder how could I plot both histograms at the same time with different colors

User · Answer

Here is a simple method to plot two histograms  with their bars side-by-side  on the same plot when the data has different sizes    def plotHistogram p  o               p and o are iterables with the values you want to      plot the histogram of             plt hist  p  o   color   g   r    alpha 0 8  bins 50      plt show

User · Answer

Also an option which is quite similar to joaquin answer   import random from matplotlib import pyplot   random data x    random gauss 3 1  for   in range 400   y    random gauss 4 2  for   in range 400     plot both histograms range from -10 to 10   bins set to 100 pyplot hist  x y   bins  100  range  -10 10   alpha 0 5  label   x    y     plot legend pyplot legend loc  upper right    show it pyplot show     Gives the following output

User · Answer

It sounds like you might want just a bar graph     http   matplotlib sourceforge net examples pylab examples bar stacked html http   matplotlib sourceforge net examples pylab examples barchart demo html   Alternatively  you can use subplots

User · Answer

You should use bins from the values returned by hist   import numpy as np import matplotlib pyplot as plt  foo   np random normal loc 1  size 100    a normal distribution bar   np random normal loc -1  size 10000    a normal distribution     bins      plt hist foo  bins 50  range  -6  6   normed True      plt hist bar  bins bins  alpha 0 5  normed True

User · Answer

There is one caveat when you want to plot the histogram from a 2-d numpy array  You need to swap the 2 axes   import numpy as np import matplotlib pyplot as plt  data   np random normal size  2  300     swapped data shape     300  2  swapped data   np swapaxes x  axis1 0  axis2 1  plt hist swapped data  bins 30  label   x    y    plt legend   plt show

User · Answer

This question has been answered before  but wanted to add another quick easy workaround that might help other visitors to this question   import seasborn as sns  sns kdeplot mydata1  sns kdeplot mydata2    Some helpful examples are here for kde vs histogram comparison

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Here you have a working example   import random import numpy from matplotlib import pyplot  x    random gauss 3 1  for   in range 400   y    random gauss 4 2  for   in range 400    bins   numpy linspace -10  10  100   pyplot hist x  bins  alpha 0 5  label  x   pyplot hist y  bins  alpha 0 5  label  y   pyplot legend loc  upper right   pyplot show

User · Answer

The accepted answers gives the code for a histogram with overlapping bars  but in case you want each bar to be side-by-side  as I did   try the variation below   import numpy as np import matplotlib pyplot as plt plt style use  seaborn-deep    x   np random normal 1  2  5000  y   np random normal -1  3  2000  bins   np linspace -10  10  30   plt hist  x  y   bins  label   x    y    plt legend loc  upper right   plt show       Reference  http   matplotlib org examples statistics histogram demo multihist html  EDIT  2018 03 16   Updated to allow plotting of arrays of different sizes  as suggested by  stochastic zeitgeist

User · Answer

Just in case you have pandas  import pandas as pd  or are ok with using it   test   pd DataFrame   random gauss 3 1  for   in range 400                           random gauss 4 2  for   in range 400     plt hist test values T  plt show

User · Answer

Inspired by Solomon s answer  but to stick with the question  which is related to histogram  a clean solution is   sns distplot bar  sns distplot foo  plt show     Make sure to plot the taller one first  otherwise you would need to set plt ylim 0 0 45  so that the taller histogram is not chopped off

User · Answer

In the case you have different sample sizes  it may be difficult to compare the distributions with a single y-axis  For example   import numpy as np import matplotlib pyplot as plt   makes the data y1   np random normal -2  2  1000  y2   np random normal 2  2  5000  colors     b   g     plots the histogram fig  ax1   plt subplots   ax1 hist  y1 y2  color colors  ax1 set xlim -10 10  ax1 set ylabel  Count   plt tight layout   plt show       In this case  you can plot your two data sets on different axes  To do so  you can get your histogram data using matplotlib  clear the axis  and then re-plot it on two separate axes  shifting the bin edges so that they don t overlap     sets up the axis and gets histogram data fig  ax1   plt subplots   ax2   ax1 twinx   ax1 hist  y1  y2   color colors  n  bins  patches   ax1 hist  y1 y2   ax1 cla    clear the axis   plots the histogram data width    bins 1  - bins 0     0 4 bins shifted   bins   width ax1 bar bins  -1   n 0   width  align  edge   color colors 0   ax2 bar bins shifted  -1   n 1   width  align  edge   color colors 1     finishes the plot ax1 set ylabel  Count   color colors 0   ax2 set ylabel  Count   color colors 1   ax1 tick params  y   colors colors 0   ax2 tick params  y   colors colors 1   plt tight layout   plt show

User · Answer

As a completion to Gustavo Bezerra s answer   If you want each histogram to be normalized  normed for mpl lt  2 1 and density for mpl  3 1  you cannot just use normed density True  you need to set the weights for each value instead   import numpy as np import matplotlib pyplot as plt  x   np random normal 1  2  5000  y   np random normal -1  3  2000  x w   np empty x shape  x w fill 1 x shape 0   y w   np empty y shape  y w fill 1 y shape 0   bins   np linspace -10  10  30   plt hist  x  y   bins  weights  x w  y w   label   x    y    plt legend loc  upper right   plt show       As a comparison  the exact same x and y vectors with default weights and density True

User · Answer

Plotting two overlapping histograms  or more  can lead to a rather cluttered plot  I find that using step histograms  aka hollow histograms  improves the readability quite a bit  The only downside is that in matplotlib the default legend for a step histogram is not properly formatted  so it can be edited like in the following example  import numpy as np                     v 1 19 2 import matplotlib pyplot as plt        v 3 3 2 from matplotlib lines import Line2D  rng   np random default rng seed 123     Create two normally distributed random variables of different sizes   and with different shapes data1   rng normal loc 30  scale 10  size 500  data2   rng normal loc 50  scale 10  size 1000     Create figure with  step  type of histogram to improve plot readability fig  ax   plt subplots figsize  9 5   ax hist  data1  data2   bins 15  histtype  step   linewidth 2          alpha 0 7  label   data1   data2       Edit legend to get lines as legend keys instead of the default polygons   and sort the legend entries in alphanumeric order handles  labels   ax get legend handles labels   leg entries      for h  label in zip handles  labels       leg entries label    Line2D  0    0   color h get facecolor    -1                                   alpha h get alpha    lw h get linewidth    labels sorted  lines   zip  sorted leg entries items     ax legend lines  labels sorted  frameon False     Remove spines ax spines  top   set visible False  ax spines  right   set visible False     Add annotations plt ylabel  Frequency   labelpad 15  plt title  Matplotlib step histogram   fontsize 14  pad 20  plt show     As you can see  the result looks quite clean  This is especially useful when overlapping even more than two histograms  Depending on how the variables are distributed  this can work for up to around 5 overlapping distributions  More than that would require the use of another type of plot  such as one of those presented here

[python] Plot two histograms on single chart with matplotlib

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