histogram

I'm trying to build a histogram script that compares values within, say, 20% of some "focus" value. So, for a focus value of 75, I'd look at values ranging from 60 to 90. I want a predetermined, odd number of bins/bars, with the middle bin/bar containing the focus value (75). Some bins may have a count of zero. My problem and question has to do with how to control the number of bins, and the number of ticks. I want the ticks between the bars. I want to say "7 bins" and get 7 bars, with 8 ticks. Is there any way to control bins and ticks to that level? It always seems like d3 will override me

I am trying to figure out what the right parameter in the hist function in R does. The documentation is unfortunately unclear to someone without a deep understanding of statistics such as myself. The documentation as stated online is: right logical; if TRUE, the histograms cells are right-closed (left open) intervals. What does it mean to be right-closed (or left open) intervals? When creating histograms of non-categorial data (things like pH, temperature, etc.), you need to specify things called "bins". Each bin has something called an interval specified for it. For example, if I have the

I have a large two dimensional array arr which I would like to bin over the second axis using numpy. Because np.histogram flattens the array I'm currently using a for loop: import numpy as np arr = np.random.randn(100, 100) nbins = 10 binned = np.empty((arr.shape[0], nbins)) for i in range(arr.shape[0]): binned[i,:] = np.histogram(arr[i,:], bins=nbins)[0] I feel like there should be a more direct and more efficient way to do that within numpy but I failed to find one. You could use np.apply_along_axis : x = np.array([range(20), range(1, 21), range(2, 22)]) nbins = 2 >>> np.apply_along_axis

I want to visualize the amount of different colors of a bitmap file. My Datasheet looks like: 1 163073164 4 185122087 3 255242000 8 255255255 3 000162232 1 181230029 1 127127127 1 136000021 3 200191231 I want to draw each color bar with its own color by using gnu plot histogram style. I just try something out by using "lc variable" but it doesnt work. :-( My GNUPLOT script by now: set style data histograms set boxwidth 1 set grid set style histogram cluster gap 0 set style fill solid 1.0 border -1 set yrange [0:*] set xrange [0:*] set xtics border in scale 0,10 nomirror rotate by -45 offset

How to plot a histogram with pandas DataFrame.hist() using group by? I have a data frame with 5 columns: "A", "B", "C", "D" and "Group" There are two Groups classes: "yes" and "no" Using: df.hist() I get the hist for each of the 4 columns. Now I would like to get the same 4 graphs but with blue bars (group="yes") and red bars (group = "no"). I tried this withouth success: df.hist(by = "group") This is not the most flexible workaround but will work for your question specifically. def sephist(col): yes = df[df['group'] == 'yes'][col] no = df[df['group'] == 'no'][col] return yes, no for num,