在 xy 散点图中添加标签 plot 和 seaborn

Question

I've spent hours on trying to do what I thought was a simple task, which is to add labels onto an XY plot while using seaborn.

Here's my code

import seaborn as sns
import matplotlib.pyplot as plt
%matplotlib inline

df_iris=sns.load_dataset("iris") 

sns.lmplot('sepal_length', # Horizontal axis
           'sepal_width', # Vertical axis
           data=df_iris, # Data source
           fit_reg=False, # Don't fix a regression line
           size = 8,
           aspect =2 ) # size and dimension

plt.title('Example Plot')
# Set x-axis label
plt.xlabel('Sepal Length')
# Set y-axis label
plt.ylabel('Sepal Width')

I would like to add to each dot on the plot the text in "species" column.

I've seen many examples using matplotlib but not using seaborn.

Any ideas? Thank you.

Answer 1

One way you can do this is as follows:

import seaborn as sns
import matplotlib.pyplot as plt
import pandas as pd
%matplotlib inline

df_iris=sns.load_dataset("iris") 

ax = sns.lmplot('sepal_length', # Horizontal axis
           'sepal_width', # Vertical axis
           data=df_iris, # Data source
           fit_reg=False, # Don't fix a regression line
           size = 10,
           aspect =2 ) # size and dimension

plt.title('Example Plot')
# Set x-axis label
plt.xlabel('Sepal Length')
# Set y-axis label
plt.ylabel('Sepal Width')


def label_point(x, y, val, ax):
    a = pd.concat({'x': x, 'y': y, 'val': val}, axis=1)
    for i, point in a.iterrows():
        ax.text(point['x']+.02, point['y'], str(point['val']))

label_point(df_iris.sepal_length, df_iris.sepal_width, df_iris.species, plt.gca())  

Answer 2

Here's a more up-to-date answer that doesn't suffer from the string issue described in the comments.

import seaborn as sns
import matplotlib.pyplot as plt
%matplotlib inline

df_iris=sns.load_dataset("iris") 

plt.figure(figsize=(20,10))
p1 = sns.scatterplot('sepal_length', # Horizontal axis
       'sepal_width', # Vertical axis
       data=df_iris, # Data source
       size = 8,
       legend=False)  

for line in range(0,df_iris.shape[0]):
     p1.text(df_iris.sepal_length[line]+0.01, df_iris.sepal_width[line], 
     df_iris.species[line], horizontalalignment='left', 
     size='medium', color='black', weight='semibold')

plt.title('Example Plot')
# Set x-axis label
plt.xlabel('Sepal Length')
# Set y-axis label
plt.ylabel('Sepal Width')

Answer 3

Thanks to the 2 other answers, here is a function scatter_text that makes it possible to reuse these plots several times.

import seaborn as sns
import matplotlib.pyplot as plt

def scatter_text(x, y, text_column, data, title, xlabel, ylabel):
    """Scatter plot with country codes on the x y coordinates
       Based on this answer: https://stackoverflow.com/a/54789170/2641825"""
    # Create the scatter plot
    p1 = sns.scatterplot(x, y, data=data, size = 8, legend=False)
    # Add text besides each point
    for line in range(0,data.shape[0]):
         p1.text(data[x][line]+0.01, data[y][line], 
                 data[text_column][line], horizontalalignment='left', 
                 size='medium', color='black', weight='semibold')
    # Set title and axis labels
    plt.title(title)
    plt.xlabel(xlabel)
    plt.ylabel(ylabel)
    return p1

Use the function as follows:

df_iris=sns.load_dataset("iris") 
plt.figure(figsize=(20,10))
scatter_text('sepal_length', 'sepal_width', 'species',
             data = df_iris, 
             title = 'Iris sepals', 
             xlabel = 'Sepal Length (cm)',
             ylabel = 'Sepal Width (cm)')

See also this answer on how to have a function that returns a plot: https://stackoverflow.com/a/43926055/2641825

Answer 4

Below is a solution that does not iterate over rows in the data frame using the dreaded for loop.

There are many issues regarding iterating over a data frame.

The answer is don't iterate! See this link .

The solution below relies on a function ( plotlabel ) within the petalplot function, which is called by df.apply .

Now, I know readers will comment on the fact that I use scatter and not lmplot , but that is a bit besides the point.

import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt
%matplotlib inline

df_iris=sns.load_dataset("iris") 

def petalplot(df): 
    
    def plotlabel(xvar, yvar, label):
        ax.text(xvar+0.002, yvar, label)
        
    fig = plt.figure(figsize=(30,10))
    ax = sns.scatterplot(x = 'sepal_length', y = 'sepal_width', data=df)

    # The magic starts here:
    df.apply(lambda x: plotlabel(x['sepal_length'],  x['sepal_width'], x['species']), axis=1)

    plt.title('Example Plot')
    plt.xlabel('Sepal Length')
    plt.ylabel('Sepal Width')
    
petalplot(df_iris)

Answer 5

Same idea with Scott Boston's answer , however with Seaborn v0.12+, you can leverage seaborn.FacetGrid.apply to add labels on plots and set up your figure in one go:

import seaborn as sns
import pandas as pd

%matplotlib inline

sns.set_theme()

df_iris = sns.load_dataset("iris")

(
    sns.lmplot(
        data=df_iris,
        x="sepal_length",
        y="sepal_width",
        fit_reg=False,
        height=8,
        aspect=2
    )
    .apply(lambda grid: [
        grid.ax.text(r["sepal_length"]+.02, r["sepal_width"], r["species"])
        for r in df_iris.to_dict(orient="records")
    ])
    .set(title="Example Plot")
    .set_axis_labels("Sepal Length", "Sepal Width")
)

Or, if you don't need to use lmplot , also from v0.12, you can use the seaborn.objects interface . This way we don't need to manually iterate over the Iris dataframe nor refer to df_iris or column names sepal_... multiple times.

import seaborn.objects as so

(
    so.Plot(df_iris, x="sepal_length", y="sepal_width", text="species")
        .add(so.Dot())
        .add(so.Text(halign="left"))
        .label(title="Example plot", x="Sepal Length", y="Sepal Width")
        .layout(size=(20, 10))
)

This produces the below figure:

Answer 6

Use the powerful declarative API to avoid loops ( seaborn>=0.12 ).

Specifically, put x,y, and annotations into a pandas data frame and call plotting.

Here is an example from my own research work.

import seaborn.objects as so
import pandas as pd

df = pd.DataFrame(..,columns=['phase','P(X=1)','text'])

fig,ax = plt.subplots()
    p = so.Plot(df,x='phase',y='P(X=1)',text='text').add(so.Dot(marker='+')).add(so.Text(halign='left'))
    p.on(ax).show()