How to plot two columns of a pandas data frame using points

You can specify the style of the plotted line when calling df.plot:

df.plot(x='col_name_1', y='col_name_2', style='o')

The style argument can also be a dict or list, e.g.:

import numpy as npimport pandas as pdd = {'one' : np.random.rand(10),     'two' : np.random.rand(10)}df = pd.DataFrame(d)df.plot(style=['o','rx'])

All the accepted style formats are listed in the documentation of matplotlib.pyplot.plot.

For this (and most plotting) I would not rely on the Pandas wrappers to matplotlib. Instead, just use matplotlib directly:

import matplotlib.pyplot as pltplt.scatter(df['col_name_1'], df['col_name_2'])plt.show() # Depending on whether you use IPython or interactive mode, etc.

and remember that you can access a NumPy array of the column's values with df.col_name_1.values for example.

I ran into trouble using this with Pandas default plotting in the case of a column of Timestamp values with millisecond precision. In trying to convert the objects to datetime64 type, I also discovered a nasty issue: < Pandas gives incorrect result when asking if Timestamp column values have attr astype >.

Pandas uses matplotlib as a library for basic plots. The easiest way in your case will using the following:

import pandas as pdimport numpy as np#creating sample data sample_data={'col_name_1':np.random.rand(20),      'col_name_2': np.random.rand(20)}df= pd.DataFrame(sample_data)df.plot(x='col_name_1', y='col_name_2', style='o')

However, I would recommend to use seaborn as an alternative solution if you want have more customized plots while not going into the basic level of matplotlib. In this case you the solution will be following:

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)}df= pd.DataFrame(sample_data)sns.scatterplot(x="col_name_1", y="col_name_2", data=df)