Seaborn: Avoid plotting missing values (line plot)

import numpy as npimport pandas as pdfrom matplotlib import pyplot as pltimport seaborn as sns# Make example datas = """2018-01-012018-01-02,1002018-01-03,1052018-01-042018-01-05,952018-01-06,902018-01-07,802018-01-082018-01-09"""df = pd.DataFrame([row.split(",") for row in s.split("\n")], columns=["Date", "Data"])df = df.replace("", np.nan)df["Date"] = pd.to_datetime(df["Date"])df["Data"] = df["Data"].astype(float)

Three options:

1) Use pandas or matplotlib.

2) If you need seaborn: not what it's for but for regular dates like yours you can use pointplot out of the box.

fig, ax = plt.subplots(figsize=(10, 5))plot = sns.pointplot(    ax=ax,    data=df, x="Date", y="Data")ax.set_xticklabels([])plt.show()

3) If you need seaborn and you need lineplot: I've looked at the source code and it looks like lineplot drops nans from the DataFrame before plotting. So unfortunately it's not possible to do it properly. You could use some advanced hackery though and use the hue argument to put the separate sections in separate buckets. We number the sections using the occurrences of nans.

fig, ax = plt.subplots(figsize=(10, 5))plot = sns.lineplot(    ax=ax,    data=df, x="Date", y="Data",    hue=df["Data"].isna().cumsum(), palette=["black"]*sum(df["Data"].isna()), legend=False, markers=True)ax.set_xticklabels([])plt.show()

Unfortunately the markers argument appears to be broken currently so you'll need to fix it if you want to see dates that have nans on either side.

Try setting NaN values to np.inf -- Seaborn doesn't draw those points, and doesn't connect the points before with points after.

Based on Denziloe answer:

there are three options:

1) Use pandas or matplotlib.

2) If you need seaborn: not what it's for but for regular dates like abovepointplot can use out of the box.

fig, ax = plt.subplots(figsize=(10, 5))plot = sns.pointplot(    ax=ax,    data=df, x="Date", y="Data")ax.set_xticklabels([])plt.show()

graph built on data from the question will look as below:

Pros:

• easy to implement
• an outlier in the data which is surrounded by None will be easy to notice on the graph

Cons:

• it takes a long time to generate such a graph (compared to lineplot)
• when there are many points it becomes hard to read such graphs

3) If you need seaborn and you need lineplot: hue argument can be used to put the separate sections in separate buckets. We number the sections using the occurrences of nans.

fig, ax = plt.subplots(figsize=(10, 5))plot = sns.lineplot(    ax=ax    , data=df, x="Date", y="Data"    , hue=df["Data"].isna().cumsum()    , palette=["blue"]*sum(df["Data"].isna())    , legend=False, markers=True)ax.set_xticklabels([])plt.show()

Pros:

• lineplot
• easy to read
• generated faster than point plot

Cons:

• an outlier in the data which is surrounded by None will not be drawn on the chart

The graph will look as below: