Pandas logarithmic histogram

Question

I have a pandas DataFrame with length data in seconds. The length varies from seconds to several months, therefore, taking a histogram after the log is convenient, it is better suited for the range. Here is a sample code

%matplotlib inline import numpy as np import pandas as pd x=np.random.lognormal(mean=10, sigma=1, size=10000) df=pd.DataFrame(x, range(10000), columns=['timeLength']) np.log10(df.timeLength).hist() 

However, labels on the x axis are scaled. Is there any way to put them as 10 ^ 1 and so on. Or even better if I could put them as 1 second, 10 seconds, 1 minute, 10 minutes, 1 hour, 1 day, etc.

Thanks,

Answer 1

Uneven bin histogram

Instead of logging values,

 np.log10(df.timeLength) 

try creating uneven binning when calculating the histogram. This can be done using the np.histogram bins argument .

Based on

if I could post them as 1 second, 10 seconds, 1 minute, 10 minutes, 1 hour, 1 day, etc.

the following bin array can be created

 # Bin locations (time in seconds) bins = np.array([0, 1, 10, 60, 60*10, 60*60, 24*60*60]) 

Example

The original data set was enlarged to fill more bins ( mean=5, sigma=2 instead of mean=10, sigma=1 ), this, for example, only. Non-uniform bunkers are determined, a calculated histogram and a graph are presented. Letters, for example, are subject to change.

 # Create random data in DataFrame x = np.random.lognormal(mean=5, sigma=2, size=10000) df = pd.DataFrame(x, columns=['timeLength']) print df.describe() print # Create non-uniform bins. Unit in seconds. bins = np.array([0, 1, 10, 60, 60*10, 60*60, 24*60*60]) print 'hisogram bins:', bins # Get histogram of random data y, x = np.histogram(df, bins=bins, normed=True) # Correct bin placement x = x[1:] # Turn into pandas Series hist = pd.Series(y, x) # Plot ax = hist.plot(kind='bar', width=1, alpha=0.5, align='center') ax.set_title('Non-Uniform Bin Histogram') ax.set_xlabel('Time Length') ax.set_xticklabels(['1 s', '10 s', '1 Min', '1 Hr', '1 Day', '>1 Day'], rotation='horizontal') 

---

  timeLength count 10000.000000 mean 1014.865417 std 4751.820312 min 0.062893 25% 36.941388 50% 144.081235 75% 556.223797 max 237838.467337 hisogram bins: [ 0 1 10 60 600 3600 86400] 

Please inform if this is not the intended result.

Answer 2

If you want to use custom bins, you can combine pd.cut with .groupby().count() and use the bar chart

 x=np.random.lognormal(mean=10, sigma=1, size=10000) df=pd.DataFrame(x, range(10000), columns=['timeLength']) df['bin'] = pd.cut(df.timeLength,include_lowest=True, bins=[0, 1, 10, 60, 60**2, 60**2*24, df.timeLength.max()], labels=['1s', '10s', '1min', '1hr', '1d', '>1d']) df.groupby('bin').count().plot.bar()