making matplotlib scatter plots from dataframes in Python's pandas

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 + 100In [5]: dfOut[5]:        col1      col2  col30 -1.000075 -0.759910   1001  0.510382  0.972615   2002  1.872067 -0.731010   5003  0.131612  1.075142  10004  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)

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)

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 > 300subset_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()

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()

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 + 100df.plot.scatter('col1', 'col2', df['col3'])

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 pdimport seaborn as snsimport 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")