Can I plot a linear regression with datetimes on the x-axis with seaborn?

Question

My DataFrame object looks like

            amount
date    
2014-01-06  1
2014-01-07  1
2014-01-08  4
2014-01-09  1
2014-01-14  1

I would li

Answer 1

note: this has a lot in common with Ian Thompson's answer but the approach is different enough to have it be a separate answer. I use the DataFrame format provided in the question and avoid changing the index.

Seaborn and other libraries don't deal as well with datetime axes as you might like them to. Here's how I'd work around it:

Start by adding a column of date ordinals

Seaborn will deal better with these than with dates. This is a handy trick for doing all kind of mathy things with dates and libraries that don't love dates.

df['date_ordinal'] = pd.to_datetime(df['date']).apply(lambda date: date.toordinal())

Make a plot with the ordinals on the date axis

ax = seaborn.regplot(
    data=df,
    x='date_ordinal',
    y='amount',
)
# Tighten up the axes for prettiness
ax.set_xlim(df['date_ordinal'].min() - 1, df['date_ordinal'].max() + 1)
ax.set_ylim(0, df['amount'].max() + 1)

Replace the ordinal X-axis labels with nice, readable dates

ax.set_xlabel('date')
new_labels = [date.fromordinal(int(item)) for item in ax.get_xticks()]
ax.set_xticklabels(new_labels)

ta-daa!

Answer 2

Since Seaborn has trouble with dates, I'm going to create a work-around. First, I'll make the Date column my index:

# Make dataframe
df = pd.DataFrame({'amount' : [1,
                               1,
                               4,
                               1,
                               1]},
                  index = ['2014-01-06',
                           '2014-01-07',
                           '2014-01-08',
                           '2014-01-09',
                           '2014-01-14'])

Second, convert the index to pd.DatetimeIndex:

# Make index pd.DatetimeIndex
df.index = pd.DatetimeIndex(df.index)

And replace the original with it:

# Make new index
idx = pd.date_range(df.index.min(), df.index.max())

Third, reindex with the new index (idx):

# Replace original index with idx
df = df.reindex(index = idx)

This will produce a new dataframe with NaN values for the dates you don't have data:

Fourth, since Seaborn doesn't play nice with dates and regression lines I'll create a row count column that we can use as our x-axis:

# Insert row count
df.insert(df.shape[1],
          'row_count',
          df.index.value_counts().sort_index().cumsum())

Fifth, we should now be able to plot a regression line using 'row_count' as our x variable and 'amount' as our y variable:

# Plot regression using Seaborn
fig = sns.regplot(data = df, x = 'row_count', y = 'amount')

Sixth, if you would like the dates to be along the x-axis instead of the row_count you can set the x-tick labels to the index:

# Change x-ticks to dates
labels = [item.get_text() for item in fig.get_xticklabels()]

# Set labels for 1:10 because labels has 11 elements (0 is the left edge, 11 is the right
# edge) but our data only has 9 elements
labels[1:10] = df.index.date

# Set x-tick labels
fig.set_xticklabels(labels)

# Rotate the labels so you can read them
plt.xticks(rotation = 45)

# Change x-axis title
plt.xlabel('date')

plt.show();

Hope this helps!