带有时间序列数据的Panda中的数据框

Question

I just started learning pandas. I came across this;

d = date_range('1/1/2011', periods=72, freq='H')
s = Series(randn(len(rng)), index=rng)

I have understood what is the above data means and I tried with IPython:

import numpy as np
from numpy.random import randn
import time
r = date_range('1/1/2011', periods=72, freq='H')
r
len(r)
[r[i] for i in range(len(r))]
s = Series(randn(len(r)), index=r)
s
s.plot()
df_new = DataFrame(data = s, columns=['Random Number Generated'])

Is it correct way of creating a data frame?

The Next step given is to : Return a series where the absolute difference between a number and the next number in the series is less than 0.5

Do I need to find the difference between each random number generated and store only the sets where the abs diff is < 0.5 ? Can someone explain how can I do that in pandas?

Also I tried to plot the series as histogram with;

 df_new.diff().hist()

The graph display the x as Random number with Y axis 0 to 18 (which I don't understand). Can some one explain this to me as well?

Answer 1

To give you some pointers in addition to @Dthal's comments:

r = pd.date_range('1/1/2011', periods=72, freq='H')

As commented by @Dthal, you can simplify the creation of your DataFrame randomly sampled from the normal distribution like so:

df = pd.DataFrame(index=r, data=randn(len(r)), columns=['Random Number Generated'])

To show only values that differ by less than 0.5 from the preceding value:

diff = df.diff()
diff[abs(diff['Random Number Generated']) < 0.5]

                     Random Number Generated
2011-01-01 02:00:00                 0.061821
2011-01-01 05:00:00                 0.463712
2011-01-01 09:00:00                -0.402802
2011-01-01 11:00:00                -0.000434
2011-01-01 22:00:00                 0.295019
2011-01-02 03:00:00                 0.215095
2011-01-02 05:00:00                 0.424368
2011-01-02 08:00:00                -0.452416
2011-01-02 09:00:00                -0.474999
2011-01-02 11:00:00                 0.385204
2011-01-02 12:00:00                -0.248396
2011-01-02 14:00:00                 0.081890
2011-01-02 17:00:00                 0.421897
2011-01-02 18:00:00                 0.104898
2011-01-03 05:00:00                -0.071969
2011-01-03 15:00:00                 0.101156
2011-01-03 18:00:00                -0.175296
2011-01-03 20:00:00                -0.371812

Can simplify using .dropna() to get rid of the missing values.

The pandas.Series.hist() docs inform that the default number of bins is 10 , so that's number of bars you should expect and so it turns out in this case roughly symmetric around zero ranging roughly [-4, +4] .

Series.hist(by=None, ax=None, grid=True, xlabelsize=None, xrot=None, ylabelsize=None, yrot=None, figsize=None, bins=10, **kwds) diff.hist()