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making matplotlib scatter plots from dataframes in Python's pandas

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Can you plot pandas DataFrame with Matplotlib?

Matplotlib is an amazing python library which can be used to plot pandas dataframe.

What is the method used in Matplotlib to generate scatter plots?

Scatter plots are used to observe relationship between variables and uses dots to represent the relationship between them. The scatter() method in the matplotlib library is used to draw a scatter plot.


Try passing columns of the DataFrame directly to matplotlib, as in the examples below, instead of extracting them as numpy arrays.

df = pd.DataFrame(np.random.randn(10,2), columns=['col1','col2'])
df['col3'] = np.arange(len(df))**2 * 100 + 100

In [5]: df
Out[5]: 
       col1      col2  col3
0 -1.000075 -0.759910   100
1  0.510382  0.972615   200
2  1.872067 -0.731010   500
3  0.131612  1.075142  1000
4  1.497820  0.237024  1700

Vary scatter point size based on another column

plt.scatter(df.col1, df.col2, s=df.col3)
# OR (with pandas 0.13 and up)
df.plot(kind='scatter', x='col1', y='col2', s=df.col3)

enter image description here

Vary scatter point color based on another column

colors = np.where(df.col3 > 300, 'r', 'k')
plt.scatter(df.col1, df.col2, s=120, c=colors)
# OR (with pandas 0.13 and up)
df.plot(kind='scatter', x='col1', y='col2', s=120, c=colors)

enter image description here

Scatter plot with legend

However, the easiest way I've found to create a scatter plot with legend is to call plt.scatter once for each point type.

cond = df.col3 > 300
subset_a = df[cond].dropna()
subset_b = df[~cond].dropna()
plt.scatter(subset_a.col1, subset_a.col2, s=120, c='b', label='col3 > 300')
plt.scatter(subset_b.col1, subset_b.col2, s=60, c='r', label='col3 <= 300') 
plt.legend()

enter image description here

Update

From what I can tell, matplotlib simply skips points with NA x/y coordinates or NA style settings (e.g., color/size). To find points skipped due to NA, try the isnull method: df[df.col3.isnull()]

To split a list of points into many types, take a look at numpy select, which is a vectorized if-then-else implementation and accepts an optional default value. For example:

df['subset'] = np.select([df.col3 < 150, df.col3 < 400, df.col3 < 600],
                         [0, 1, 2], -1)
for color, label in zip('bgrm', [0, 1, 2, -1]):
    subset = df[df.subset == label]
    plt.scatter(subset.col1, subset.col2, s=120, c=color, label=str(label))
plt.legend()

enter image description here


There is little to be added to Garrett's great answer, but pandas also has a scatter method. Using that, it's as easy as

df = pd.DataFrame(np.random.randn(10,2), columns=['col1','col2'])
df['col3'] = np.arange(len(df))**2 * 100 + 100
df.plot.scatter('col1', 'col2', df['col3'])

plotting sizes in col3 to col1-col2


I will recommend to use an alternative method using seaborn which more powerful tool for data plotting. You can use seaborn scatterplot and define colum 3 as hue and size.

Working code:

import pandas as pd
import seaborn as sns
import numpy as np

#creating sample data 
sample_data={'col_name_1':np.random.rand(20),
      'col_name_2': np.random.rand(20),'col_name_3': np.arange(20)*100}
df= pd.DataFrame(sample_data)
sns.scatterplot(x="col_name_1", y="col_name_2", data=df, hue="col_name_3",size="col_name_3")

enter image description here