How to Create a Multiline Plot Using Seaborn

How do I create a multiline plot using seaborn?

Seaborn favors the "long format" as input. The key ingredient to convert your DataFrame from its "wide format" (one column per measurement type) into long format (one column for all measurement values, one column to indicate the type) is pandas.melt. Given a data_preproc structured like yours, filled with random values:

num_rows = 20
years = list(range(1990, 1990 + num_rows))
data_preproc = pd.DataFrame({
    'Year': years, 
    'A': np.random.randn(num_rows).cumsum(),
    'B': np.random.randn(num_rows).cumsum(),
    'C': np.random.randn(num_rows).cumsum(),
    'D': np.random.randn(num_rows).cumsum()})

A single plot with four lines, one per measurement type, is obtained with

sns.lineplot(x='Year', y='value', hue='variable', 
             data=pd.melt(data_preproc, ['Year']))

(Note that 'value' and 'variable' are the default column names returned by melt, and can be adapted to your liking.)

Creating multiple lines in lineplot in seaborn from dataset

How about transposing you DataFrame?

import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns

df = pd.DataFrame(np.random.rand(3, 10)).T
print(df)

DataFrame:

          0         1         2
0  0.341005  0.925077  0.541746
1  0.324769  0.558320  0.902804
2  0.647871  0.630663  0.607212
3  0.722298  0.745091  0.630445
4  0.209836  0.386576  0.076790
5  0.347911  0.237178  0.446102
6  0.174991  0.777129  0.109934
7  0.022564  0.388223  0.464438
8  0.359771  0.722761  0.837942
9  0.091696  0.474859  0.840078

Plot

sns.lineplot(data=df.iloc[:, :2])

Result:

plot multiple lines in seaborn lineplot

IMO, it's a bit of a mess on a single plot, but here we go. You can use one of the default categorial color maps and a dictionary to get a single color for each model. You can use groupby to plot each model separately and knowing there are 3 lines for each we can plan to cycle the linestyles to achieve different linestyles for each of the columns.

The default legend will be total garbage, so we can construct it ourselves to indicate what model each color represents and what linestyle is used for each measurement.

import matplotlib.pyplot as plt
from matplotlib.patches import Patch
from matplotlib.lines import Line2D

colors = dict(zip(df.model.unique(), plt.cm.tab10.colors))
linestyles = ["-", "--", "-."]
ycols = ['precision', 'recall', 'f1']

# Construct legend ourself
legend_elements = ([Patch(facecolor=color, label=model)
                    for model,color in colors.items()]
                   + [Line2D((0,1),(0,0), color='black', linestyle=linestyle, label=col)
                     for linestyle,col in zip(linestyles, ycols)])
 
fix, ax = plt.subplots()
ax.set_prop_cycle(plt.cycler('linestyle', linestyles))
for model, gp in df.groupby('model'):
    gp.plot(x='K', y=ycols,
            ax=ax,
            color=colors[model],
            legend=False)

ax.legend(handles=legend_elements, bbox_to_anchor=(1,1))
plt.show()

How to create a multi-line plot for my dataset?

Reorganize df which is pivot table to classic data frame and plot as you want:

import matplotlib.pylab as plt
import pandas as pd
import seaborn as sns

df = pd.DataFrame(columns=["Event1", "Event2", "Event3"], 
                                data=[[15,1,22],
                                      [16,1.26,80],
                                      [27,0,15]])
df = df.set_index([["Series1", "Series2", "Series3"]])
print(df)

# reorganize df to classic table
df2=df.stack().reset_index()
df2.columns = ['Series','Event','Values']
print(df2)

plt.figure(figsize=(12,8))
ax = sns.pointplot(x='Event', y='Values', hue='Series',data=df2)
ax.grid(b=True, which='major', color='#d3d3d3', linewidth=1.0)
ax.grid(b=True, which='minor', color='#d3d3d3', linewidth=0.5)
plt.show()

df2:

    Series   Event  Values
0  Series1  Event1   15.00
1  Series1  Event2    1.00
2  Series1  Event3   22.00
3  Series2  Event1   16.00
4  Series2  Event2    1.26
5  Series2  Event3   80.00
6  Series3  Event1   27.00
7  Series3  Event2    0.00
8  Series3  Event3   15.00

How to plot a multiple line plot in Seaborn using specific columns?

Here I assume that the years are strings and the values are integers.

Seaborn expects data in tidy format, so first you might transform your dataframe.

import seaborn as sns
import pandas as pd


df = pd.DataFrame(
    [
        {"name": "A", "2015": 99, "2016": 98, "2017": 95, "2018": 99},
        {"name": "B", "2015": 76, "2016": 89, "2017": 83, "2018": 85},
        {"name": "C", "2015": 88, "2016": 89, "2017": 91, "2018": 91},
    ]
)

df.set_index("name", inplace=True)
tidy = df.stack().reset_index().rename(columns={"level_1": "year", 0: "values"})

Here is the outcome

    name    year    values
0   A   2015    99
1   A   2016    98
2   A   2017    95
3   A   2018    99
4   B   2015    76
5   B   2016    89
6   B   2017    83
7   B   2018    85
8   C   2015    88
9   C   2016    89
10  C   2017    91
11  C   2018    91

Then, you can

1. filter it for the years you want
2. pass it as a data parameter
3. specify the columns at x, y, and hue

sns.lineplot(
    data=tidy[tidy["year"].isin(["2016", "2018"])], x="year", y="values", hue="name"
)

The result

create seaborn lineplot with multiple lines and hue

For hue to work properly, all data should be provided at once. Also, hue should refer to an array of the same length as x and y, so repeating the line title for each entry in the arrays of x and y values.

Here is a possible adaption of the first few lines of save_graph:

import matplotlib.pyplot as plt
import seaborn as sns
import numpy as np

def save_graph(properties):
    plt.figure()
    x_values = np.concatenate([iteration_values.x_values 
                               for iteration_values in properties.graph_iteration_values])
    y_values = np.concatenate([iteration_values.y_values
                               for iteration_values in properties.graph_iteration_values])
    line_titles = np.concatenate([[iteration_values.line_title] * len(iteration_values.x_values)
                                  for iteration_values in properties.graph_iteration_values])
    sns_plot = sns.lineplot(x=x_values, y=y_values, hue=line_titles)
    ...

Another option is to draw multiple plots on the same graph, not using hue but set the label:

def save_graph(properties):
    plt.figure()
    ax = None
    for iteration_values in properties.iteration_values:
        ax = sns.lineplot(x=iteration_values.x_values, y=iteration_values.y_values,
                                label=iteration_values.line_title, ax=ax)
    ...

This will loop through the current color cycle and also create the correct legend.

Plot multiple line graph from Pandas into Seaborn

The seaborn lineplot() documentation says:

Passing the entire wide-form dataset to data plots a separate line for each column

Since you want a line for each row instead, you need to transpose your dataframe, so try this:

sns.lineplot(data=ibd_panda_conv.T, dashes=False)

Seaborn multi line plot with only one line colored

You can use pandas groupby to plot:

fig,ax=plt.subplots()
for c,d in df.groupby('country'):
    color = 'red' if c=='US' else 'grey'
    d.plot(x='year',y='pop', ax=ax, color=color)

ax.legend().remove()

output:

Or you can define a specific palette as a dictionary:

palette = {c:'red' if c=='US' else 'grey' for c in df.country.unique()}

sns.lineplot(x='year', y='pop', data=df, hue='country', 
             palette=palette, legend=False)

Output:

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