smooth plotting all columns of a data-frame

Question

I have a data frame of:

Index   Date        AA   BB   CC     DD    EE   FF
0       2019-01-15  0.0  -1.0  0.0   0.0   0.0  2.0
1       2019-01-17  0.0  -1.0  -1.0  -1.0  0.0  2.0
2       2019-01-22  1.0  -1.0  1.0   -1.0  0.0  2.0
3       2019-01-24  0.0  0.0   0.0   0.0   0.0  2.0
4       2019-01-29  1.0  0.0   -1.0  0.0   -1.0 2.0
5       2019-01-31  0.0  -1.0  0.0   0.0   0.0  2.0
6       2019-02-05  1.0  1.0   1.0   0.0   1.0  2.0
7       2019-02-12  2.0  1.0   1.0   0.0   2.0  2.0

which I'm plotting with:

dfs = dfs.melt('Date', var_name = 'cols', value_name = 'vals')
ax = sns.lineplot(x = "Date", y = 'vals', hue = 'cols', 
                  style = 'cols', markers = True, dashes = False, data = dfs)
ax.set_xticklabels(dfs['Date'].dt.strftime('%d-%m-%Y'))
plt.xticks(rotation = -90)
plt.tight_layout()
plt.show()

resulting:

which is ugly. I want to have the markers in the exact place as what is in the data-frame but the lines to be smoothed. I'm aware of scipy -> spline (e.g. here), however that seems to be too much hassle to convert all the columns. There is also Pandas -> resample -> interpolate (e.g. here) which is very close to what I want but I have to turn the Date column to index which I don't want to do...

I would appreciate if you could help me know what is the best Pythonic way to do this.

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P.S. A complete version of my code can be seen here.

Answer 1

I think you need to write a custom plotting function that iterates over all columns and plots interpolated data to specified axes instance. Look at the following code:

import pandas as pd
import numpy as np

# data = pd.read_clipboard()
# data.drop(['Index'], axis=1, inplace=True)

def add_smooth_plots(df, ax,  timecolumn='Date', interpolation_method='cubic', colors='rgbky'):
    from itertools import cycle
    ind = pd.to_datetime(df.loc[:, timecolumn])
    tick_labels =ind.dt.strftime("%Y-%m-%d")
    color = cycle(colors)
    for i, col in enumerate(df.columns):
        if col != timecolumn:
            c = next(color)
            s = pd.Series(df.loc[:, col].values, index=ind)
            intp = s.resample('0.5D').interpolate(method=interpolation_method)
            true_ticks = intp.index.isin(ind)
            vals = intp.values
            intp = intp.reset_index()
            ticks = intp.index[true_ticks]
            ax.plot(np.arange(len(vals)), vals, label=col, color=c)
            ax.set_xticks(ticks)
            ax.set_xticklabels(tick_labels.values, rotation=45)
            ax.legend(title='Columns')
    return ax

from matplotlib import pyplot as plt
fig = plt.figure()
ax = fig.add_subplot(111)

add_smooth_plots(data, ax)

plt.show()