Plot multiple columns of Pandas DataFrame using Seaborn

• Some seaborn plots will accept a wide dataframe, sns.pointplot(data=df, x='X_Axis', y='col_2'), but not sns.pointplot(data=df, x='X_Axis', y=['col_2', 'col_3']), so it's better to reshape the DataFrame.
• Reshape the DataFrame from wide to long with pandas.DataFrame.melt.
  • Converting the dataframe from a wide to long form is standard for all seaborn plots, not just the examples shown.
• Tested in python 3.8.12, pandas 1.3.4, matplotlib 3.4.3, seaborn 0.11.2

Sample DataFrame

import pandas as pd
import seaborn as sns

df = pd.DataFrame({'X_Axis':[1,3,5,7,10,20],
                   'col_2':[.4,.5,.4,.5,.5,.4],
                   'col_3':[.7,.8,.9,.4,.2,.3],
                   'col_4':[.1,.3,.5,.7,.1,.0],
                   'col_5':[.5,.3,.6,.9,.2,.4]})

# display(df)
   X_Axis  col_2  col_3  col_4  col_5
0       1    0.4    0.7    0.1    0.5
1       3    0.5    0.8    0.3    0.3
2       5    0.4    0.9    0.5    0.6
3       7    0.5    0.4    0.7    0.9
4      10    0.5    0.2    0.1    0.2
5      20    0.4    0.3    0.0    0.4

# convert to long (tidy) form
dfm = df.melt('X_Axis', var_name='cols', value_name='vals')

# display(dfm.head())
   X_Axis   cols  vals
0       1  col_2   0.4
1       3  col_2   0.5
2       5  col_2   0.4
3       7  col_2   0.5
4      10  col_2   0.5

Current Plot Methods

catplot: figure-level

Use seaborn.catplot with kind= (e.g. kind='point' to reproduce the FactorPlot default):

g = sns.catplot(x="X_Axis", y="vals", hue='cols', data=dfm, kind='point')

pointplot: axes-level

sns.pointplot(x="X_Axis", y="vals", hue='cols', data=dfm)

Original

factorplot: was renamed to catplot v0.9.0 (July 2018)

New versions of seaborn get warning:

The factorplot function has been renamed to catplot. The original name will be removed in a future release. Please update your code. Note that the default kind in factorplot ('point') has changed 'strip' in catplot.

g = sns.factorplot(x="X_Axis", y="vals", hue='cols', data=dfm)

# using pd.melt instead of pd.DataFrame.melt for pandas < 0.20.0
# dfm = pd.melt(df, 'X_Axis', var_name='cols',  value_name='vals')
# g = sns.factorplot(x="X_Axis", y="vals", hue='cols', data=dfm)

in addition to mighty @jezrael for those who come from google if you intend to plot lines with the index of the original dataframe just do as follows:

df = pd.DataFrame({'col_2':[.4,.5,.4,.5,.5,.4],
                   'col_3':[.7,.8,.9,.4,.2,.3],
                   'col_4':[.1,.3,.5,.7,.1,.0],
                   'col_5':[.5,.3,.6,.9,.2,.4]})

# resetting index before melting to save the current index in 'index' column...
df = df.reset_index().melt('index', var_name='cols',  value_name='vals')
g = sns.catplot(x="index", y="vals", hue='cols', data=df, kind='point')