4.1 Trends

1. Quick start

import pandas as pd
import numpy as np
import matplotlib.pyplot as plt​
import seaborn as  sns
sns.set(rc={'figure.figsize':(12,6)},font_scale=1.3, style='whitegrid',palette ='muted')

You can set aesthetic parameters in one step by sns.set()

• font_scale: Separate scaling factor to independently scale the size of the font elements.

• style: dict, None, or one of {darkgrid, whitegrid, dark, white, ticks}

• palette: Color palette, see color_palette()​

You can set aesthetic parameters in one step by sns.set()

• font_scale: Separate scaling factor to independently scale the size of the font elements.

• style: dict, None, or one of {darkgrid, whitegrid, dark, white, ticks}

• palette: Color palette, see color_palette()​

2. Simple plot

Firstly, let us create a simple dataset. The head of the dataset looks as below:

# create a dataset
index = pd.date_range("1 1 2012", periods=100, freq="m", name="date")
data = np.random.randn(100, 4).cumsum(axis=0)
df = pd.DataFrame(data, index, ["a", "b", "c", "d"])

Then, we can plot a very simple line chart. Since we've already set all the aesthetic parameters at the beginning, here the code is pretty short and clean.

sns.lineplot(data=df)

We can not only plot a full dataset, but also pieces of the dataset. For example, I will truncate two lists of data from the original dataset. One is in a different period, the other is in the same period. Then we put them together by plt.subplots()‌

• a and b are different lines in different periods.

• c and c are different lines in the same period.

list1 = [df.loc[:"2015", "a"], df.loc["2015":, "b"]]
list2 = [df.loc[:"2015", "c"], df.loc[:"2015", "d"]]

​f,axes  = plt.subplots(2,1)      # create a subplot, 2 row and 1 column  
sns.lineplot(data=list1, ax=axes[0])   # plot list 1  
sns.lineplot(data=list2, ax=axes[1])   # plot list 2

3. Smart plot

More complex datasets will have multiple measurements for the same value of the x variable. The default behavior in seaborn is to aggregate the multiple measurements at each x value by plotting the mean and the 95% confidence interval ( grey area) around the mean. Let us take the "fmri" dataset for example.

fmri = sns.load_dataset("fmri")                 # import embedded dataset 
sns.lineplot(x="timepoint", y="signal",  data=fmri)        # line plot

The relationship between x and y can be shown for different subsets of the data using the hue, size, and style parameters. These parameters control what visual semantics are used to identify the different subsets.

• hue: Grouping variable that will produce lines with different colors. Can be either categorical or numeric, although color mapping will behave differently in the latter case.

• size: Grouping variable that will produce lines with different widths. Can be either categorical or numeric, although size mapping will behave differently in the latter case.

• style: Grouping variable that will produce lines with different dashes and/or markers. Can have a numeric type but will always be treated as categorical.

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For example, seaborn groups variables in the "fmri" dataset and show the groups with different colors, and use color and line dashing to represent two different grouping variables.

# Plot the responses for different events and regions
sns.lineplot(x="timepoint", y="signal", data=fmri, hue="region", style = "event")

4. Facet plot

The chart above is quite complicated and not user-friendly. People need to read slowly and carefully to get the information. In this case, we need to consider another way to deliver the same information but more clear.‌

​FacetGridis a useful approach is to draw multiple instances of the same plot on different subsets of the dataset. It allows a viewer to quickly extract a large amount of information about complex data.‌

We can Use relplot() to combine lineplot() and FacetGrid​

sns.relplot(x="timepoint", y="signal", data=fmri,
                col="region", hue="event", style="event", kind="line")