Remove interpolation Time series plot for missing values

Question

I'm trying to plot a time series data but I have some problems.

I'm using this code:

from matplotlib import pyplot as plt
plt.figure('Fig')
plt.plot(data.index,data.Colum,'g', linewidth=2.0,label='Data')

And I get this:

But I dont want the interpolation between missing values!

How can I achieve this?

Answer 1

Since you are using pandas you could do something like this:

import pandas as pd
import matplotlib.pyplot as plt

pd.np.random.seed(1234)
idx = pd.date_range(end=datetime.today().date(), periods=10, freq='D')
vals = pd.Series(pd.np.random.randint(1, 10, size=idx.size), index=idx)
vals.iloc[4:8] = pd.np.nan
print vals

Here is an example of a column from a DataFrame with DatetimeIndex

2016-03-29    4.0
2016-03-30    7.0
2016-03-31    6.0
2016-04-01    5.0
2016-04-02    NaN
2016-04-03    NaN
2016-04-04    NaN
2016-04-05    NaN
2016-04-06    9.0
2016-04-07    1.0
Freq: D, dtype: float64

To plot it without dates where data is NaN you could do something like this:

fig, ax = plt.subplots()
ax.plot(range(vals.dropna().size), vals.dropna())
ax.set_xticklabels(vals.dropna().index.date.tolist());
fig.autofmt_xdate()

Which should produce a plot like this:

The trick here is to replace the dates with some range of values that do not trigger matplotlib's internal date processing when you call .plot method.

Later, when the plotting is done, replace the ticklabels with actual dates. Optionally, call .autofmt_xdate() to make labels readable.