Plotting time-series data with seaborn

Question

Say I create a fully random `Dataframe` using the following: ``` from pandas.util import testing from random import randrange def random_date(start, end): delta = end - start int_delta = (delta.days * 24 * 60 * 60) + delta.seconds random_second = randrange(int_delta) return start + timedelta(seconds=random_second) def rand_dataframe(): df = testing.makeDataFrame() df['date'] = [random_date(datetime.date(2014,3,18),datetime.date(2014,4,1)) for x in xrange(df.shape[0])] df.sort(columns=['date'], inplace=True) return df df = rand_dataframe() ``` which results in the dataframe shown at the bottom of this post. I would like to plot my columns `A`, `B`, `C` and `D` using the timeseries visualization features in `seaborn` so that I get something along these lines: How can I approach this problem? From what I read on this notebook, the call should be: ``` sns.tsplot(df, time="time", unit="unit", condition="condition", value="value") ``` but this seems to require that the dataframe is represen…

Accepted Answer

I don't think `tsplot` is going to work with the data you have. The assumptions it makes about the input data are that you've sampled the same units at each timepoint (although you can have missing timepoints for some units).

For example, say you measured blood pressure from the same people every day for a month, and then you wanted to plot the average blood pressure by condition (where maybe the "condition" variable is the diet they are on). `tsplot` could do this, with a call that would look something like `sns.tsplot(df, time="day", unit="person", condition="diet", value="blood_pressure")`

That scenario is different from having large groups of people on different diets and each day randomly sampling some from each group and measuring their blood pressure. From the example you gave, it seems like your data are structured like the this.

However, it's not that hard to come up with a mix of matplotlib and pandas that will do what I think you want:

```
# Read in the data from the stackoverflow question
df = pd.read_clipboard().iloc[1:]

# Convert it to "long-form" or "tidy" representation
df = pd.melt(df, id_vars=["date"], var_name="condition")

# Plot the average value by condition and date
ax = df.groupby(["condition", "date"]).mean().unstack("condition").plot()

# Get a reference to the x-points corresponding to the dates and the the colors
x = np.arange(len(df.date.unique()))
palette = sns.color_palette()

# Calculate the 25th and 75th percentiles of the data
# and plot a translucent band between them
for cond, cond_df in df.groupby("condition"):
    low = cond_df.groupby("date").value.apply(np.percentile, 25)
    high = cond_df.groupby("date").value.apply(np.percentile, 75)
    ax.fill_between(x, low, high, alpha=.2, color=palette.pop(0))
```

This code produces:

Plotting time-series data with seaborn

Solution

Problem