I’m currently learning about data visualization using seaborn, and I came across a problem that I couldn’t find a solution to.
import pandas as pd import matplotlib.pyplot as plt import seaborn as sns %matplotlib inline
So I have this data
| index | col1 | col2 | col3 | col4 | col5 | col6 | col7 | col8 |
|---|---|---|---|---|---|---|---|---|
| 1990 | 0 | 4 | 7 | 3 | 7 | 0 | 6 | 6 |
| 1991 | 1 | 7 | 5 | 0 | 8 | 1 | 8 | 4 |
| 1992 | 0 | 5 | 0 | 1 | 9 | 1 | 7 | 2 |
| 1993 | 2 | 7 | 0 | 0 | 6 | 1 | 2 | 7 |
| 1994 | 4 | 1 | 5 | 5 | 8 | 1 | 6 | 3 |
| 1995 | 7 | 0 | 6 | 4 | 8 | 0 | 5 | 7 |
| 1996 | 5 | 1 | 1 | 4 | 6 | 1 | 7 | 4 |
| 1997 | 0 | 4 | 7 | 5 | 5 | 1 | 8 | 5 |
| 1998 | 1 | 3 | 7 | 0 | 7 | 0 | 7 | 1 |
| 1999 | 5 | 7 | 1 | 1 | 6 | 0 | 8 | 5 |
| 2000 | 3 | 8 | 5 | 0 | 3 | 0 | 6 | 3 |
| 2001 | 6 | 0 | 4 | 1 | 7 | 1 | 2 | 7 |
I want to make barplots/histplots with col1, col2 .. col8 as one column and 1990 values as one column so like 1990;
| col? | val |
|---|---|
| col1 | 0 |
| col2 | 4 |
| col3 | 7 |
| col4 | 3 |
| col5 | 7 |
| col6 | 0 |
| col7 | 6 |
| col8 | 6 |
and plot them for each year from 1990 to 2001.
g = sns.FacetGrid(df, col=df.index.value_counts()) g.map(sns.histplot, df.columns)
This is the code that I’ve written I looked at facetgrid but could get it working for my case, any feedback is appreciated.
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Answer
Imports and Test DataFrame
- Tested with
pandas 1.3.0,matplotlib 3.4.2, andseaborn 0.11.1
import pandas as pd
import seaborn as sns
# sample dataframe
data = {1990: {'col1': 0, 'col2': 4, 'col3': 7, 'col4': 3, 'col5': 7, 'col6': 0, 'col7': 6, 'col8': 6}, 1991: {'col1': 1, 'col2': 7, 'col3': 5, 'col4': 0, 'col5': 8, 'col6': 1, 'col7': 8, 'col8': 4}, 1992: {'col1': 0, 'col2': 5, 'col3': 0, 'col4': 1, 'col5': 9, 'col6': 1, 'col7': 7, 'col8': 2}, 1993: {'col1': 2, 'col2': 7, 'col3': 0, 'col4': 0, 'col5': 6, 'col6': 1, 'col7': 2, 'col8': 7}, 1994: {'col1': 4, 'col2': 1, 'col3': 5, 'col4': 5, 'col5': 8, 'col6': 1, 'col7': 6, 'col8': 3}, 1995: {'col1': 7, 'col2': 0, 'col3': 6, 'col4': 4, 'col5': 8, 'col6': 0, 'col7': 5, 'col8': 7}, 1996: {'col1': 5, 'col2': 1, 'col3': 1, 'col4': 4, 'col5': 6, 'col6': 1, 'col7': 7, 'col8': 4}, 1997: {'col1': 0, 'col2': 4, 'col3': 7, 'col4': 5, 'col5': 5, 'col6': 1, 'col7': 8, 'col8': 5}, 1998: {'col1': 1, 'col2': 3, 'col3': 7, 'col4': 0, 'col5': 7, 'col6': 0, 'col7': 7, 'col8': 1}, 1999: {'col1': 5, 'col2': 7, 'col3': 1, 'col4': 1, 'col5': 6, 'col6': 0, 'col7': 8, 'col8': 5}, 2000: {'col1': 3, 'col2': 8, 'col3': 5, 'col4': 0, 'col5': 3, 'col6': 0, 'col7': 6, 'col8': 3}, 2001: {'col1': 6, 'col2': 0, 'col3': 4, 'col4': 1, 'col5': 7, 'col6': 1, 'col7': 2, 'col8': 7}}
df = pd.DataFrame.from_dict(data, orient='index')
# display(df.head())
col1 col2 col3 col4 col5 col6 col7 col8
1990 0 4 7 3 7 0 6 6
1991 1 7 5 0 8 1 8 4
1992 0 5 0 1 9 1 7 2
1993 2 7 0 0 6 1 2 7
1994 4 1 5 5 8 1 6 3
Plotting with seaborn.catplot
- Using
seaborn 0.11.1, the correct way to create abarplotFacetGrid (per the documentation), is withsns.catplotandkind='bar'. - It is required to convert the dataframe from a wide to long form, which is easily done by resetting the index, and then using
pandas.DataFrame.melt() - A
catplotis a figure-level interface for drawing categorical plots onto a FacetGrid.g.set_xticklabels(rotation=90)can be used to rotate the xticklabels.- See How to rotate xticklabels in a seaborn catplot or How to set rotation for seaborn FacetGrid and figure-level xtick labels
# convert the wide dataframe to a long format with melt dfm = df.reset_index().melt(id_vars='index', var_name='variable', value_name='value') # display(dfm.head()) index variable value 0 1990 col1 0 1 1991 col1 1 2 1992 col1 0 3 1993 col1 2 4 1994 col1 4 # plot with catplot and kind='bar' g = sns.catplot(data=dfm, kind='bar', col='index', col_wrap=4, x='variable', y='value', height=3) # change the ticklabel rotation if needed g.set_xticklabels(rotation=90) # change ylim if needed g.set(ylim=(0, 30))
Plotting with pandas.DataFrame.plot
- While you have asked about
seaborn, given the dataframe in the OP with all the years in the index, the easiest way to plot the data is transpose the dataframe with.T, and then usepandas.DataFrame.plot
# display(df.T.head())
1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001
col1 0 1 0 2 4 7 5 0 1 5 3 6
col2 4 7 5 7 1 0 1 4 3 7 8 0
col3 7 5 0 0 5 6 1 7 7 1 5 4
col4 3 0 1 0 5 4 4 5 0 1 0 1
col5 7 8 9 6 8 8 6 5 7 6 3 7
# transpose and plot
axes = df.T.plot(kind='bar', subplots=True, layout=[3, 4], figsize=(15, 7), legend=False, rot=0)
# to change ylim of the subplots, if needed
for ax in axes.flatten():
ax.set_ylim(0, 30)
