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.

Plot two histograms on single chart with matplotlib

I am using R and I have two data frames: carrots and cucumbers. Each data frame has a single numeric column that lists the length of all measured carrots (total: 100k carrots) and cucumbers (total: 50k cucumbers).

I wish to plot two histograms - carrot length and cucumbers lengths - on the same plot. They overlap, so I guess I also need some transparency. I also need to use relative frequencies not absolute numbers since the number of instances in each group is different. 

Something like this would be nice but I don&#39;t understand how to create it from my two tables:

![overlapped density][1]


  [1]: http://i.stack.imgur.com/nn4JT.png

How to plot two histograms together in R?

I know how to create a histogram (just use &quot;with boxes&quot;) in gnuplot if my .dat file already has properly binned data. Is there a way to take a list of numbers and have gnuplot provide a histogram based on ranges and bin sizes the user provides?

Histogram using gnuplot?

I&#39;m using matplotlib to make a histogram.

Is there any way to manually set the size of the bins as opposed to the number of bins?

Bin size in Matplotlib (Histogram)

Is there a way of creating scatterplots with marginal histograms just like in the sample below in `ggplot2`? In Matlab it is the `scatterhist()` function and there exist equivalents for R as well. However, I haven&#39;t seen it for ggplot2.

![scatterplot with marginal histograms][1]

I started an attempt by creating the single graphs but don&#39;t know how to arrange them properly.

     require(ggplot2)
     x&lt;-rnorm(300)
     y&lt;-rt(300,df=2)
     xy&lt;-data.frame(x,y)
         xhist &lt;- qplot(x, geom=&quot;histogram&quot;) + scale_x_continuous(limits=c(min(x),max(x))) + opts(axis.text.x = theme_blank(), axis.title.x=theme_blank(), axis.ticks = theme_blank(), aspect.ratio = 5/16, axis.text.y = theme_blank(), axis.title.y=theme_blank(), background.colour=&quot;white&quot;)
         yhist &lt;- qplot(y, geom=&quot;histogram&quot;) + coord_flip() + opts(background.fill = &quot;white&quot;, background.color =&quot;black&quot;)

         yhist &lt;- yhist + scale_x_continuous(limits=c(min(x),max(x))) + opts(axis.text.x = theme_blank(), axis.title.x=theme_blank(), axis.ticks = theme_blank(), aspect.ratio = 16/5, axis.text.y = theme_blank(), axis.title.y=theme_blank() )


         scatter &lt;- qplot(x,y, data=xy)  + scale_x_continuous(limits=c(min(x),max(x))) + scale_y_continuous(limits=c(min(y),max(y)))
    none &lt;- qplot(x,y, data=xy) + geom_blank()


and arranging them with the function posted [here][2]. But to make  long story short: Is there a way of creating these graphs?


  [1]: http://i.stack.imgur.com/uKiQS.jpg
  [2]: http://gettinggeneticsdone.blogspot.com/2010/03/arrange-multiple-ggplot2-plots-in-same.html

Scatterplot with marginal histograms in ggplot2

It is really straightforward to see and understand the scalar values in TensorBoard. However, it&#39;s not clear how to understand histogram graphs. 

For example, they are the histograms of my network weights.

[![enter image description here][1]][2]

(After fixing a bug thanks to sunside)
[![enter image description here][2]][2]
What is the best way to interpret these? Layer 1 weights look mostly flat, what does this mean?

I added the network construction code here.

    X = tf.placeholder(tf.float32, [None, input_size], name=&quot;input_x&quot;)
    x_image = tf.reshape(X, [-1, 6, 10, 1])
    tf.summary.image(&#39;input&#39;, x_image, 4)
    
    # First layer of weights
    with tf.name_scope(&quot;layer1&quot;):
        W1 = tf.get_variable(&quot;W1&quot;, shape=[input_size, hidden_layer_neurons],
                             initializer=tf.contrib.layers.xavier_initializer())
        layer1 = tf.matmul(X, W1)
        layer1_act = tf.nn.tanh(layer1)
        tf.summary.histogram(&quot;weights&quot;, W1)
        tf.summary.histogram(&quot;layer&quot;, layer1)
        tf.summary.histogram(&quot;activations&quot;, layer1_act)
    
    # Second layer of weights
    with tf.name_scope(&quot;layer2&quot;):
        W2 = tf.get_variable(&quot;W2&quot;, shape=[hidden_layer_neurons, hidden_layer_neurons],
                             initializer=tf.contrib.layers.xavier_initializer())
        layer2 = tf.matmul(layer1_act, W2)
        layer2_act = tf.nn.tanh(layer2)
        tf.summary.histogram(&quot;weights&quot;, W2)
        tf.summary.histogram(&quot;layer&quot;, layer2)
        tf.summary.histogram(&quot;activations&quot;, layer2_act)
    
    # Third layer of weights
    with tf.name_scope(&quot;layer3&quot;):
        W3 = tf.get_variable(&quot;W3&quot;, shape=[hidden_layer_neurons, hidden_layer_neurons],
                             initializer=tf.contrib.layers.xavier_initializer())
        layer3 = tf.matmul(layer2_act, W3)
        layer3_act = tf.nn.tanh(layer3)
    
        tf.summary.histogram(&quot;weights&quot;, W3)
        tf.summary.histogram(&quot;layer&quot;, layer3)
        tf.summary.histogram(&quot;activations&quot;, layer3_act)
    
    # Fourth layer of weights
    with tf.name_scope(&quot;layer4&quot;):
        W4 = tf.get_variable(&quot;W4&quot;, shape=[hidden_layer_neurons, output_size],
                             initializer=tf.contrib.layers.xavier_initializer())
        Qpred = tf.nn.softmax(tf.matmul(layer3_act, W4)) # Bug fixed: Qpred = tf.nn.softmax(tf.matmul(layer3, W4))
        tf.summary.histogram(&quot;weights&quot;, W4)
        tf.summary.histogram(&quot;Qpred&quot;, Qpred)
    
    # We need to define the parts of the network needed for learning a policy
    Y = tf.placeholder(tf.float32, [None, output_size], name=&quot;input_y&quot;)
    advantages = tf.placeholder(tf.float32, name=&quot;reward_signal&quot;)
    
    # Loss function
    # Sum (Ai*logp(yi|xi))
    log_lik = -Y * tf.log(Qpred)
    loss = tf.reduce_mean(tf.reduce_sum(log_lik * advantages, axis=1))
    tf.summary.scalar(&quot;Q&quot;, tf.reduce_mean(Qpred))
    tf.summary.scalar(&quot;Y&quot;, tf.reduce_mean(Y))
    tf.summary.scalar(&quot;log_likelihood&quot;, tf.reduce_mean(log_lik))
    tf.summary.scalar(&quot;loss&quot;, loss)
    
    # Learning
    train = tf.train.AdamOptimizer(learning_rate=learning_rate).minimize(loss)


  [1]: https://i.stack.imgur.com/RVOA5.jpg
  [2]: https://i.stack.imgur.com/IttNH.jpg

Understanding TensorBoard (weight) histograms

While reading up on numpy, I encountered the function [`numpy.histogram()`][1].

What is it for and **how does it work?** In the docs they mention **bins**: What are they?

Some googling led me to the [definition of Histograms in general][2]. I get that. But unfortunately I can&#39;t link this knowledge to the examples given in the docs.


  [1]: http://docs.scipy.org/doc/numpy/reference/generated/numpy.histogram.html
  [2]: http://en.wikipedia.org/wiki/Histogram

How does numpy.histogram() work?

So I have a little problem. I have a data set in scipy that is already in the histogram format, so I have the center of the bins and the number of events per bin. How can I now plot is as a histogram. I tried just doing 

    bins, n=hist()

but it didn&#39;t like that. Any recommendations?

Histogram Matplotlib

In ipython Notebook, first create a pandas Series object, then by calling the instance method .hist(), the browser displays the figure. 

I am wondering how to save this figure to a file (I mean not by right click and save as, but the commands needed in the script).

save a pandas.Series histogram plot to file

Is there a function in R that fits a curve to a histogram?

Let&#39;s say you had the following histogram

    hist(c(rep(65, times=5), rep(25, times=5), rep(35, times=10), rep(45, times=4)))

It looks normal, but it&#39;s skewed. I want to fit a normal curve that is skewed to wrap around this histogram.

This question is rather basic, but I can&#39;t seem to find the answer for R on the internet.

Fitting a density curve to a histogram in R

I&#39;d like to plot a normalized histogram from a vector using `matplotlib`.  I tried the following:

    plt.hist(myarray, normed=True)

as well as:

    plt.hist(myarray, normed=1)

but neither option produces a y-axis from [0, 1] such that the bar heights of the histogram sum to 1.

Plot a histogram such that bar heights sum to 1 (probability)

As far as I know the option Log=True in the histogram function only refers to the y-axis.

    P.hist(d,bins=50,log=True,alpha=0.5,color=&#39;b&#39;,histtype=&#39;step&#39;)

I need the bins to be equally spaced in log10. Is there something that can do this?

How to have logarithmic bins in a Python histogram

Is there a way to specify bin sizes in MySQL? Right now, I am trying the following SQL query:

    select total, count(total) from faults GROUP BY total;

The data that is being generated is good enough but there are just too many rows. What I need is a way to group the data into predefined bins. I can do this from a scripting language, but is there a way to do it directly in SQL?

Example:

    +-------+--------------+
    | total | count(total) |
    +-------+--------------+
    |    30 |            1 | 
    |    31 |            2 | 
    |    33 |            1 | 
    |    34 |            3 | 
    |    35 |            2 | 
    |    36 |            6 | 
    |    37 |            3 | 
    |    38 |            2 | 
    |    41 |            1 | 
    |    42 |            5 | 
    |    43 |            1 | 
    |    44 |            7 | 
    |    45 |            4 | 
    |    46 |            3 | 
    |    47 |            2 | 
    |    49 |            3 | 
    |    50 |            2 | 
    |    51 |            3 | 
    |    52 |            4 | 
    |    53 |            2 | 
    |    54 |            1 | 
    |    55 |            3 | 
    |    56 |            4 | 
    |    57 |            4 | 
    |    58 |            2 | 
    |    59 |            2 | 
    |    60 |            4 | 
    |    61 |            1 | 
    |    63 |            2 | 
    |    64 |            5 | 
    |    65 |            2 | 
    |    66 |            3 | 
    |    67 |            5 | 
    |    68 |            5 | 
    ------------------------

What I am looking for:

    +------------+---------------+
    | total      | count(total)  |
    +------------+---------------+
    |    30 - 40 |            23 | 
    |    40 - 50 |            15 | 
    |    50 - 60 |            51 | 
    |    60 - 70 |            45 | 
    ------------------------------

I guess this cannot be achieved in a straight forward manner but a reference to any related stored procedure would be fine as well.

Getting data for histogram plot

I&#39;m currently using Matplotlib to create a histogram:

![enter image description here][1]

    import matplotlib
    matplotlib.use(&#39;Agg&#39;)
    import matplotlib.pyplot as pyplot
    ...
    fig = pyplot.figure()
    ax = fig.add_subplot(1,1,1,)
    n, bins, patches = ax.hist(measurements, bins=50, range=(graph_minimum, graph_maximum), histtype=&#39;bar&#39;)

    #ax.set_xticklabels([n], rotation=&#39;vertical&#39;)

    for patch in patches:
        patch.set_facecolor(&#39;r&#39;)

    pyplot.title(&#39;Spam and Ham&#39;)
    pyplot.xlabel(&#39;Time (in seconds)&#39;)
    pyplot.ylabel(&#39;Bits of Ham&#39;)
    pyplot.savefig(output_filename)


I&#39;d like to make the x-axis labels a bit more meaningful.

Firstly, the x-axis ticks here seem to be limited to five ticks. No matter what I do, I can&#39;t seem to change this - even if I add more xticklabels, it only uses the first five. I&#39;m not sure how Matplotlib calculates this, but I assume it&#39;s auto-calculated from the range/data?

**Is there some way I can increase the resolution of x-tick labels** - even to the point of one for each bar/bin?

(Ideally, I&#39;d also like the seconds to be reformatted in micro-seconds/milli-seconds, but that&#39;s a question for another day).

Secondly, I&#39;d like **each individual bar labeled** - with the actual number in that bin, as well as the percentage of the total of all bins.

The final output might look something like this:

![enter image description here][2]

Is something like that possible with Matplotlib?

Cheers,
Victor


  [1]: http://i.stack.imgur.com/B0jJr.png
  [2]: http://i.stack.imgur.com/UyBGD.png

Matplotlib - label each bin

I&#39;m trying to generate a histogram in R with a logarithmic scale for y. Currently I do:

    hist(mydata$V3, breaks=c(0,1,2,3,4,5,25))

This gives me a histogram, but the density between 0 to 1 is so great (about a million values difference) that you can barely make out any of the other bars.

Then I&#39;ve tried doing:

    mydata_hist &lt;- hist(mydata$V3, breaks=c(0,1,2,3,4,5,25), plot=FALSE)
    plot(rpd_hist$counts, log=&quot;xy&quot;, pch=20, col=&quot;blue&quot;)

It gives me sorta what I want, but the bottom shows me the values 1-6 rather than 0, 1, 2, 3, 4, 5, 25.  It&#39;s also showing the data as points rather than bars. `barplot` works but then I don&#39;t get any bottom axis.

Histogram with Logarithmic Scale and custom breaks

I have plotted a histogram in Jupyter (Python 2) and was expecting to see the outlines of my bars but this is not the case. 

[![enter image description here][1]][1]

I&#39;m using the following code:

    import matplotlib.pyplot as plt
    from numpy.random import normal
    gaussian_numbers = normal(size=1000)
    plt.hist(gaussian_numbers)
    plt.title(&quot;Gaussian Histogram&quot;)
    plt.xlabel(&quot;Value&quot;)
    plt.ylabel(&quot;Frequency&quot;)
    plt.show()


  [1]: https://i.stack.imgur.com/r97fv.png

Python histogram outline

I need some guidance in working out how to plot a block of histograms from grouped data in a pandas dataframe. Here&#39;s an example to illustrate my question:

    from pandas import DataFrame
    import numpy as np
    x = [&#39;A&#39;]*300 + [&#39;B&#39;]*400 + [&#39;C&#39;]*300
    y = np.random.randn(1000)
    df = DataFrame({&#39;Letter&#39;:x, &#39;N&#39;:y})
    grouped = df.groupby(&#39;Letter&#39;)

In my ignorance I tried this code command:

    df.groupby(&#39;Letter&#39;).hist()

which failed with the error message &quot;TypeError: cannot concatenate &#39;str&#39; and &#39;float&#39; objects&quot;

Any help most appreciated.

Plotting histograms from grouped data in a pandas DataFrame

I have managed to find online how to overlay a normal curve to a histogram in R, but I would like to retain the normal &quot;frequency&quot; y-axis of a histogram. See two code segments below, and notice how in the second, the y-axis is replaced with &quot;density&quot;. How can I keep that y-axis as &quot;frequency&quot;, as it is in the first plot.

**AS A BONUS:** I&#39;d like to mark the SD regions (up to 3 SD) on the density curve as well. How can I do this? I tried `abline`, but the line extends to the top of the graph and looks ugly.

    g = d$mydata
    hist(g)
![enter image description here][1]

    g = d$mydata
    m&lt;-mean(g)
    std&lt;-sqrt(var(g))
    hist(g, density=20, breaks=20, prob=TRUE, 
         xlab=&quot;x-variable&quot;, ylim=c(0, 2), 
         main=&quot;normal curve over histogram&quot;)
    curve(dnorm(x, mean=m, sd=std), 
          col=&quot;darkblue&quot;, lwd=2, add=TRUE, yaxt=&quot;n&quot;)
![enter image description here][2]

See how in the image above, the y-axis is &quot;density&quot;. I&#39;d like to get that to be &quot;frequency&quot;.

  [1]: http://i.stack.imgur.com/7bl5b.png
  [2]: http://i.stack.imgur.com/1dfiE.png

Overlay normal curve to histogram in R

I have a problem making histograms from pandas series objects and I can&#39;t understand why it does not work. The code has worked fine before but now it does not.

Here is a bit of my code (specifically, a pandas series object I&#39;m trying to make a histogram of):

    type(dfj2_MARKET1[&#39;VSPD2_perc&#39;])

which outputs the result: 
    `pandas.core.series.Series`

Here&#39;s my plotting code:

    fig, axes = plt.subplots(1, 7, figsize=(30,4))
    axes[0].hist(dfj2_MARKET1[&#39;VSPD1_perc&#39;],alpha=0.9, color=&#39;blue&#39;)
    axes[0].grid(True)
    axes[0].set_title(MARKET1 + &#39;  5-40 km / h&#39;)

Error message:

        AttributeError                            Traceback (most recent call last)
        &lt;ipython-input-75-3810c361db30&gt; in &lt;module&gt;()
          1 fig, axes = plt.subplots(1, 7, figsize=(30,4))
          2 
        ----&gt; 3 axes[1].hist(dfj2_MARKET1[&#39;VSPD2_perc&#39;],alpha=0.9, color=&#39;blue&#39;)
          4 axes[1].grid(True)
          5 axes[1].set_xlabel(&#39;Time spent [%]&#39;)
    
        C:\Python27\lib\site-packages\matplotlib\axes.pyc in hist(self, x, bins, range, normed,          weights, cumulative, bottom, histtype, align, orientation, rwidth, log, color, label,    stacked, **kwargs)
       8322             # this will automatically overwrite bins,
       8323             # so that each histogram uses the same bins
    -&gt; 8324             m, bins = np.histogram(x[i], bins, weights=w[i], **hist_kwargs)
       8325             m = m.astype(float) # causes problems later if it&#39;s an int
       8326             if mlast is None:
    
        C:\Python27\lib\site-packages\numpy\lib\function_base.pyc in histogram(a, bins, range,     normed, weights, density)
        158         if (mn &gt; mx):
        159             raise AttributeError(
    --&gt; 160                 &#39;max must be larger than min in range parameter.&#39;)
        161 
        162     if not iterable(bins):
    
    AttributeError: max must be larger than min in range parameter.



Matplotlib/Pandas error using histogram

                                                                     
                                                                     
                                                                     
                                             
Main Question
---


I&#39;m having issues with understanding why the handling of dates, labels and breaks is not working as I would have expected in R when trying to make a histogram with ggplot2.

**I&#39;m looking for:**

- A histogram of the frequency of my dates
- Tick marks centered under the matching bars
- Date labels in `%Y-b` format
- Appropriate limits; minimized empty space between edge of grid space and outermost bars

I&#39;ve [uploaded my data to pastebin](http://pastebin.com/sDzXKFxJ) to make this reproducible. I&#39;ve created several columns as I wasn&#39;t sure the best way to do this:

    &gt; dates &lt;- read.csv(&quot;http://pastebin.com/raw.php?i=sDzXKFxJ&quot;, sep=&quot;,&quot;, header=T)
    &gt; head(dates)
           YM       Date Year Month
    1 2008-Apr 2008-04-01 2008     4
    2 2009-Apr 2009-04-01 2009     4
    3 2009-Apr 2009-04-01 2009     4
    4 2009-Apr 2009-04-01 2009     4
    5 2009-Apr 2009-04-01 2009     4
    6 2009-Apr 2009-04-01 2009     4

Here&#39;s what I tried:

    library(ggplot2)
    library(scales)
    dates$converted &lt;- as.Date(dates$Date, format=&quot;%Y-%m-%d&quot;)
   
    ggplot(dates, aes(x=converted)) + geom_histogram()
    +      opts(axis.text.x = theme_text(angle=90))

Which yields [this graph](http://i.imgur.com/rks0y.png). I wanted `%Y-%b` formatting, though, so I hunted around and tried the following, based on [this SO](https://stackoverflow.com/questions/10576095/formatting-dates-with-scale-x-date-in-ggplot2):

    ggplot(dates, aes(x=converted)) + geom_histogram()
    +    scale_x_date(labels=date_format(&quot;%Y-%b&quot;),
    +    breaks = &quot;1 month&quot;)
    +    opts(axis.text.x = theme_text(angle=90))
    
    stat_bin: binwidth defaulted to range/30. Use &#39;binwidth = x&#39; to adjust this.

That gives me [this graph](http://i.stack.imgur.com/HaGsV.png)

- Correct x axis label format
- The frequency distribution has changed shape (binwidth issue?)
- Tick marks don&#39;t appear centered under bars
- The xlims have changed as well

I worked through the example in the [ggplot2 documentation](http://cran.r-project.org/web/packages/ggplot2/ggplot2.pdf) at the `scale_x_date` section and `geom_line()` appears to break, label, and center ticks correctly when I use it with my same x-axis data. I don&#39;t understand why the histogram is different.

---

Updates based on answers from edgester and gauden
---

I initially thought gauden&#39;s answer helped me solve my problem, but am now puzzled after looking more closely. Note the differences between the two answers&#39; resulting graphs after the code.

Assume for both:

    library(ggplot2)
    library(scales)
    dates &lt;- read.csv(&quot;http://pastebin.com/raw.php?i=sDzXKFxJ&quot;, sep=&quot;,&quot;, header=T)

Based on @edgester&#39;s answer below, I was able to do the following:

    freqs &lt;- aggregate(dates$Date, by=list(dates$Date), FUN=length)
    freqs$names &lt;- as.Date(freqs$Group.1, format=&quot;%Y-%m-%d&quot;)
    
    ggplot(freqs, aes(x=names, y=x)) + geom_bar(stat=&quot;identity&quot;) +
           scale_x_date(breaks=&quot;1 month&quot;, labels=date_format(&quot;%Y-%b&quot;),
                        limits=c(as.Date(&quot;2008-04-30&quot;),as.Date(&quot;2012-04-01&quot;))) +
           ylab(&quot;Frequency&quot;) + xlab(&quot;Year and Month&quot;) +
           theme_bw() + opts(axis.text.x = theme_text(angle=90))
   

Here is my attempt based on gauden&#39;s answer:

    dates$Date &lt;- as.Date(dates$Date)
    ggplot(dates, aes(x=Date)) + geom_histogram(binwidth=30, colour=&quot;white&quot;) +
           scale_x_date(labels = date_format(&quot;%Y-%b&quot;),
                        breaks = seq(min(dates$Date)-5, max(dates$Date)+5, 30),
                        limits = c(as.Date(&quot;2008-05-01&quot;), as.Date(&quot;2012-04-01&quot;))) +
           ylab(&quot;Frequency&quot;) + xlab(&quot;Year and Month&quot;) +
           theme_bw() + opts(axis.text.x = theme_text(angle=90))

Plot based on edgester&#39;s approach:

![edgester-plot][1]

Plot based on gauden&#39;s approach:

![gauden-plot][2]

Note the following:

- gaps in gauden&#39;s plot for 2009-Dec and 2010-Mar; `table(dates$Date)` reveals that there are 19 instances of `2009-12-01` and 26 instances of `2010-03-01` in the data
- edgester&#39;s plot starts at 2008-Apr and ends at 2012-May. This is correct based on a minimum value in the data of 2008-04-01 and a max date of 2012-05-01. For some reason gauden&#39;s plot starts in 2008-Mar and still somehow manages to end at 2012-May. After counting bins and reading along the month labels, for the life of me I can&#39;t figure out which plot has an extra or is missing a bin of the histogram!

Any thoughts on the differences here? edgester&#39;s method of creating a separate count

---

Related References
---

As an aside, here are other locations that have information about dates and ggplot2 for passers-by looking for help:

- [Started here](http://learnr.wordpress.com/2010/02/25/ggplot2-plotting-dates-hours-and-minutes/) at learnr.wordpress, a popular R blog. It stated that I needed to get my data into POSIXct format, which I now think is false and wasted my time.
- [Another learnr post](http://learnr.wordpress.com/2009/05/05/ggplot2-two-time-series-with-different-dates/) recreates a time series in ggplot2, but wasn&#39;t really applicable to my situation.
- [r-bloggers has a post on this](http://www.r-bloggers.com/plotting-time-series-data-using-ggplot2/), but it appears outdated. The simple `format=` option did not work for me.
- [This SO question](https://stackoverflow.com/questions/6638696/breaks-for-scale-x-date-in-ggplot2-and-r) is playing with breaks and labels. I tried treating my `Date` vector as continuous and don&#39;t think it worked so well. It looked like it was overlaying the same label text over and over so the letters looked kind of odd. The distribution is sort of correct but there are odd breaks. My attempt based on the accepted answer was like so ([result here](http://i.stack.imgur.com/ntDPD.png)).


  [1]: http://i.stack.imgur.com/SQB95.png
  [2]: http://i.stack.imgur.com/qvXN5.png

Understanding dates and plotting a histogram with ggplot2 in R

I saw on another question that I could use `Counter()` to count the number of occurrences in a set of strings. So if I have `[&#39;A&#39;,&#39;B&#39;,&#39;A&#39;,&#39;C&#39;,&#39;A&#39;,&#39;A&#39;]` I get `Counter({&#39;A&#39;:3,&#39;B&#39;:1,&#39;C&#39;:1})`. But now, how can I use that information to build a histogram for example?

Using Counter() in Python to build histogram?

I&#39;m looking for advice on how to show a title at the top of a collection of histogram plots that have been generated by a pandas df.hist() command. For instance, in the histogram figure block generated by the code below I&#39;d like to place a general title (e.g. &#39;My collection of histogram plots&#39;) at the top of the figure:

    data = DataFrame(np.random.randn(500).reshape(100,5), columns=list(&#39;abcde&#39;))
    axes = data.hist(sharey=True, sharex=True)

I&#39;ve tried using the *title* keyword in the hist command (i.e. title=&#39;My collection of histogram plots&#39;), but that didn&#39;t work.

The following code *does* work (in an ipython notebook) by adding text to one of the axes, but is a bit of a kludge.

    axes[0,1].text(0.5, 1.4,&#39;My collection of histogram plots&#39;, horizontalalignment=&#39;center&#39;,
                   verticalalignment=&#39;center&#39;, transform=axes[0,1].transAxes)

Is there a better way?

add title to collection of pandas hist plots

I am trying to make a histogram of density values and overlay that with the curve of a density function (not the density estimate).

Using a simple standard normal example, here is some data:

    x &lt;- rnorm(1000)
I can do:

    q &lt;- qplot( x, geom=&quot;histogram&quot;)
    q + stat_function( fun = dnorm )
but this gives the scale of the histogram in frequencies and not densities. with `..density..` I can get the proper scale on the histogram:

    q &lt;- qplot( x,..density.., geom=&quot;histogram&quot;)
    q
But now this gives an error:

    q + stat_function( fun = dnorm )
Is there something I am not seeing?

Another question, is there a way to plot the curve of a function, like `curve()`, but then not as layer?

Overlay histogram with density curve

I am using seaborn to plot a distribution plot. I would like to plot multiple distributions on the same plot in different colors:

Here&#39;s how I start the distribution plot:

    import numpy as np
    import pandas as pd
    from sklearn.datasets import load_iris
    iris = load_iris()
    iris = pd.DataFrame(data= np.c_[iris[&#39;data&#39;], iris[&#39;target&#39;]],columns= iris[&#39;feature_names&#39;] + [&#39;target&#39;])

       sepal length (cm)  sepal width (cm)  petal length (cm)  petal width (cm)  target
    0                5.1               3.5                1.4               0.2     0.0
    1                4.9               3.0                1.4               0.2     0.0
    2                4.7               3.2                1.3               0.2     0.0
    3                4.6               3.1                1.5               0.2     0.0
    4                5.0               3.6                1.4               0.2     0.0
    
    sns.distplot(iris[[&#39;sepal length (cm)&#39;]], hist=False, rug=True);

[![enter image description here][1]][1]

The `&#39;target&#39;` column contains 3 values: 0, 1, 2.

I would like to see one distribution plot for sepal length, where `target ==0`, `target ==1`, and `target ==2`, for a total of 3 plots. 

  [1]: https://i.stack.imgur.com/JlzKB.png

seaborn distplot / displot with multiple distributions

I have made a plot using  `ggplot2` `geom_histogram` from a data frame. See sample below and link to the ggplot histogram https://stackoverflow.com/questions/25350094/need-to-label-each-geom-vline-with-the-factors-using-a-nested-ddply-function-and 

I now need to make a data frame that contains the summarized data used to generate the ggplot above.

    Sector2	Family	Year	Length
    BUN	Acroporidae	2010	332.1300496
    BUN	Poritidae	2011	141.1467966
    BUN	Acroporidae	2012	127.479
    BUN	Acroporidae	2013	142.5940556
    MUR	Faviidae	2010	304.0405
    MUR	Faviidae	2011	423.152
    MUR	Pocilloporidae	2012	576.0295
    MUR	Poritidae	2013	123.8936667
    NTH	Faviidae	2010	60.494
    NTH	Faviidae	2011	27.427
    NTH	Pocilloporidae	2012	270.475
    NTH	Poritidae	2013	363.4635


Extract data from a ggplot

For a small project, I need to compare one image with another - to determine if the images are approximately the same or not. The images are smallish, varying from 25 to 100px across. The images are meant to be of the same picture data but are sublty different, so a simple pixel equality check won&#39;t work. Consider these two possible scenarios:

1. A security (CCTV) camera in a museum looking at an exhibit: we want to quickly see if two different video frames show the same scene, but slight differences in lighting and camera focus means they won&#39;t be identical.
2. A picture of a vector computer GUI icon rendered at 64x64 compared to the same icon rendered at 48x48 (but both images would be scaled down to 32x32 so the histograms have the same total pixel count).

I&#39;ve decided to represent each image using histograms, using three 1D histograms: one for each RGB channel - it&#39;s safe for me to just use colour and to ignore texture and edge histograms (An alternative approach uses a single 3D histogram for each image, but I&#39;m avoiding that as it adds extra complexity). Therefore I will need to compare the histograms to see how similar they are, and if the similarity measure passes some threshold value then I can say with confidence the respective images are visually the same - I would be comparing each image&#39;s corresponding channel histograms (e.g. image 1&#39;s red histogram with image 2&#39;s red histogram, then image 1&#39;s blue histogram with image 2&#39;s blue histogram, then the green histograms - so I&#39;m not comparing image 1&#39;s red histogram with image 2&#39;s blue histogram, that would just be silly).

Let&#39;s say I have these three histograms, which represent a summary of the red RGB channel for three images (using 5 bins for 7-pixel images for simplicity): 

    H1            H2            H3 

      X           X                     X
      X   X       X       X             X
    X X   X X     X X   X X     X X X X X
    0 1 2 3 4     0 1 2 3 4     0 1 2 3 4

    H1 = [ 1, 3, 0, 2, 1 ]
    H2 = [ 3, 1, 0, 1, 2 ]
    H3 = [ 1, 1, 1, 1, 3 ] 

Image 1 (`H1`) is my reference image, and I want to see if Image 2 (`H2`) and/or Image 3 (`H3`) is similar to Image 1. Note that in this example, Image 2 is similar to Image 1, but Image 3 is not.

When I did a cursory search for &quot;histogram difference&quot; algorithms (at least those I could understand) I found a popular approach was to just sum the differences between each bin, however this approach often fails because it weighs all bin differences the same.

To demonstrate the problem with this approach, in C# code, like this:

    Int32[] image1RedHistogram = new Int32[] { 1, 3, 0, 2, 1 };
    Int32[] image2RedHistogram = new Int32[] { 3, 2, 0, 1, 2 };
    Int32[] image3RedHistogram = new Int32[] { 1, 1, 1, 1, 3 };

    Int32 GetDifference(Int32[] x, Int32[] y) {
        Int32 sumOfDifference = 0;
        for( int i = 0; i &lt; x.Length; i++ ) {
            sumOfDifference += Math.Abs( x[i] - y[i] );
        }
        return sumOfDifferences;
    }

The output of which is:

    GetDifference( image1RedHistogram, image2RedHistogram ) == 6
    GetDifference( image1RedHistogram, image3RedHistogram ) == 6

This is incorrect.

Is there a way to determine the difference between two histograms that takes into account the shape of the distribution?

Comparing two histograms

I am very new to R, so I apologize for such a basic question. I spent an hour googling this issue, but couldn&#39;t find a solution.

Say I have some categorical data in my data set about common pet types. I input it as a character vector in R that contains the names of different types of animals. I created it like this: 

    animals &lt;- c(&quot;cat&quot;, &quot;dog&quot;,  &quot;dog&quot;, &quot;dog&quot;, &quot;dog&quot;, &quot;dog&quot;, &quot;dog&quot;, &quot;dog&quot;, &quot;cat&quot;, &quot;cat&quot;, &quot;bird&quot;)

I turn it into a factor for use with other vectors in my data frame:

    animalFactor &lt;- as.factor(animals)

I now want to create a histogram that shows the frequency of each variable on the y-axis, the name of each factor on the x-axis, and contains one bar for each factor. I attempt this code:

    hist(table(animalFactor), freq=TRUE, xlab = levels(animalFactor), ylab = &quot;Frequencies&quot;)

The output is absolutely nothing like I&#39;d expect. Labeling problems aside, I can&#39;t seem to figure out how to create a simple frequency histogram by category. 

Make Frequency Histogram for Factor Variables

I have a histogram

    H=hist(my_data,bins=my_bin,histtype=&#39;step&#39;,color=&#39;r&#39;)

I can see that the shape is almost gaussian but I would like to fit this histogram with a gaussian function and print the value of the mean and sigma I get. Can you help me?

Fitting a histogram with python

I have a very simple question causing me to bang my head on the wall.

I would like to scale the y-axis of my histogram to reflect the proportion (0 to 1) that each bin makes up, instead of having the area of the bars sum to 1, as using y=..density.. does, or having the highest bar be 1, as y=..ncount.. does.

My input is a list of names and values, formatted like so:

    name	value
    A	0.0000354
    B	0.00768
    C	0.00309
    D	0.000123

One of my failed attempts:

    library(ggplot2)
    mydataframe &lt; read.delim(mydata)
    ggplot(mydataframe, aes(x = value)) +
    geom_histogram(aes(x=value,y=..density..))

This gives me a histogram with area 1, but heights of 2000 and 1000:

[![try][1]][1]

and y=..ncount.. gives me a histogram with highest bar 1.0, and rest scaled to it:

[![try][2]][2]

but I would like to have the first bar have a height of 0.5, and the other two 0.25.

R does not recognize these uses of scale_y_continuous either.

    scale_y_continuous(formatter=&quot;percent&quot;)
    scale_y_continuous(labels = percent)
    scale_y_continuous(expand=c(1/(nrow(mydataframe)-1),0)

Thank you for any help.

  [1]: http://i.imgur.com/nhDcF.png
  [2]: http://i.imgur.com/wog8K.png


Normalizing y-axis in histograms in R ggplot to proportion

I&#39;m trying to create a histogram of a data column and plot it logarithmically (`y-axis`) and I&#39;m  not sure why the following code does not work:

    import numpy as np
    import matplotlib.pyplot as plt
    data = np.loadtxt(&#39;foo.bar&#39;)
    fig = plt.figure()
    ax = fig.add_subplot(111)
    plt.hist(data, bins=(23.0, 23.5,24.0,24.5,25.0,25.5,26.0,26.5,27.0,27.5,28.0))
    ax.set_xlim(23.5, 28)
    ax.set_ylim(0, 30)
    ax.grid(True)
    plt.yscale(&#39;log&#39;)
    plt.show()

I&#39;ve also tried instead of `plt.yscale(&#39;log&#39;)` adding `Log=true` in the `plt.hist` line and also I tried `ax.set_yscale(&#39;log&#39;)`, but nothing seems to work. I either get an empty plot, either the `y-axis` is indeed logarithmic (with the code as shown above), but there is no data plotted (no bins). 



Logarithmic y-axis bins in python

I created a `dictionary` that counts the occurrences in a `list` of every key and I would now like to plot the histogram of its content.

This is the content of the dictionary I want to plot:

    {1: 27, 34: 1, 3: 72, 4: 62, 5: 33, 6: 36, 7: 20, 8: 12, 9: 9, 10: 6, 11: 5, 12: 8, 2: 74, 14: 4, 15: 3, 16: 1, 17: 1, 18: 1, 19: 1, 21: 1, 27: 2}

So far I wrote this:

    import numpy as np
    import matplotlib.pyplot as plt

    pos = np.arange(len(myDictionary.keys()))
    width = 1.0     # gives histogram aspect to the bar diagram
    
    ax = plt.axes()
    ax.set_xticks(pos + (width / 2))
    ax.set_xticklabels(myDictionary.keys())
    
    plt.bar(myDictionary.keys(), ******, width, color=&#39;g&#39;)
    #                            ^^^^^^ what should I put here?
    plt.show()

I tried by simply doing

    plt.bar(myDictionary.keys(), myDictionary, width, color=&#39;g&#39;)

but this is the result:

![enter image description here][1]

and I don&#39;t know why the 3 bars are shifted and also I&#39;d like the histogram to be displayed in a ordered fashion.

Can somebody tell me how to do it?

  [1]: http://i.stack.imgur.com/3lj8q.png

All Histogram Solutions on Gang of Coders

Total of 31 Histogram Solutions