Pandas Limited Range Bar Chart

Question

Is there a way to create a bar chart from continuous data encoded at predetermined intervals? For instance,

In[1]: df Out[1]: 0 0.729630 1 0.699620 2 0.710526 3 0.000000 4 0.831325 5 0.945312 6 0.665428 7 0.871845 8 0.848148 9 0.262500 10 0.694030 11 0.503759 12 0.985437 13 0.576271 14 0.819742 15 0.957627 16 0.814394 17 0.944649 18 0.911111 19 0.113333 20 0.585821 21 0.930131 22 0.347222 23 0.000000 24 0.987805 25 0.950570 26 0.341317 27 0.192771 28 0.320988 29 0.513834 231 0.342541 232 0.866279 233 0.900000 234 0.615385 235 0.880597 236 0.620690 237 0.984375 238 0.171429 239 0.792683 240 0.344828 241 0.288889 242 0.961686 243 0.094402 244 0.960526 245 1.000000 246 0.166667 247 0.373494 248 0.000000 249 0.839416 250 0.862745 251 0.589873 252 0.983871 253 0.751938 254 0.000000 255 0.594937 256 0.259615 257 0.459916 258 0.935065 259 0.969231 260 0.755814 

and instead of a simple histogram:

 df.hist() 

I need to create a bar chart where each point will count the number of instances within a predefined range. For example, the following graph should have three columns with the number of points that fall into: [0 0.35], [0.35 0.7] [0.7 1.0]

EDIT

Thanks so much for your answers. Another question is how to order bins? For example, I get the following result:

 In[349]: out.value_counts() Out[349]: [0, 0.001] 104 (0.001, 0.1] 61 (0.1, 0.2] 32 (0.2, 0.3] 20 (0.3, 0.4] 18 (0.7, 0.8] 6 (0.4, 0.5] 6 (0.5, 0.6] 5 (0.6, 0.7] 4 (0.9, 1] 3 (0.8, 0.9] 2 (1, 1.001] 0 

as you can see, the last three bins are not ordered. How to sort a data frame based on "categories" or my boxes?

EDIT 2

Just found how to solve it, just using reindex ():

 In[355]: out.value_counts().reindex(out.cat.categories) Out[355]: [0, 0.001] 104 (0.001, 0.1] 61 (0.1, 0.2] 32 (0.2, 0.3] 20 (0.3, 0.4] 18 (0.4, 0.5] 6 (0.5, 0.6] 5 (0.6, 0.7] 4 (0.7, 0.8] 6 (0.8, 0.9] 2 (0.9, 1] 3 (1, 1.001] 0 

Answer 1

You can use pd.cut to break the values ​​into cells corresponding to each interval, and then take each interval of the total count using pd.value_counts . Build a histogram later, additionally replace the labels of the X labels by the type of category to which this particular tick belongs.

 out = pd.cut(s, bins=[0, 0.35, 0.7, 1], include_lowest=True) ax = out.value_counts(sort=False).plot.bar(rot=0, color="b", figsize=(6,4)) ax.set_xticklabels([c[1:-1].replace(","," to") for c in out.cat.categories]) plt.show() 

---

If you want the Y axis to display as a relative percentage, normalize the frequency and multiply by the result 100.

 out = pd.cut(s, bins=[0, 0.35, 0.7, 1], include_lowest=True) out_norm = out.value_counts(sort=False, normalize=True).mul(100) ax = out_norm.plot.bar(rot=0, color="b", figsize=(6,4)) ax.set_xticklabels([c[1:-1].replace(","," to") for c in out.cat.categories]) plt.ylabel("pct") plt.show() 

Answer 2

You can use matplotlib to plot a histogram. Unlike pandas' hist , matplotlib.pyplot.hist accepts an array as input for bins.

 import numpy as np; np.random.seed(0) import matplotlib.pyplot as plt import pandas as pd x = np.random.rand(120) df = pd.DataFrame({"x":x}) bins= [0,0.35,0.7,1] plt.hist(df.values, bins=bins, edgecolor="k") plt.xticks(bins) plt.show() 

Answer 3

You can use pd.cut

 bins = [0,0.35,0.7,1] df = df.groupby(pd.cut(df['val'], bins=bins)).val.count() df.plot(kind='bar')