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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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2 Answers 2

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Imports and Test DataFrame

  • Tested in python 3.11.3, pandas 2.0.2, matplotlib 3.7.1, seaborn 0.12.2
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

# convert the wide dataframe to a long format with melt dfm = df.melt(ignore_index=False).reset_index(names=['Year']) # display(dfm.head()) Year 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='Year', col_wrap=4, x='variable', y='value', height=3)

enter image description here

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 use pandas.DataFrame.plot
    • Set ylim=(0, 30) if needed.
# 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)

enter image description here

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1 Comment

Sorry for the late response, thank you so much, I'm still at the stage where I need to be explained everything, your post is more clear than anything I could even imagine. Once again thank you so much
1

melt your dataframe first.

df = pd.DataFrame({'index': {0: 1990, 1: 1991, 2: 1992, 3: 1993, 4: 1994, 5: 1995, 6: 1996, 7: 1997, 8: 1998, 9: 1999, 10: 2000, 11: 2001}, 'col1': {0: 0, 1: 1, 2: 0, 3: 2, 4: 4, 5: 7, 6: 5, 7: 0, 8: 1, 9: 5, 10: 3, 11: 6}, 'col2': {0: 4, 1: 7, 2: 5, 3: 7, 4: 1, 5: 0, 6: 1, 7: 4, 8: 3, 9: 7, 10: 8, 11: 0}, 'col3': {0: 7, 1: 5, 2: 0, 3: 0, 4: 5, 5: 6, 6: 1, 7: 7, 8: 7, 9: 1, 10: 5, 11: 4}, 'col4': {0: 3, 1: 0, 2: 1, 3: 0, 4: 5, 5: 4, 6: 4, 7: 5, 8: 0, 9: 1, 10: 0, 11: 1}, 'col5': {0: 7, 1: 8, 2: 9, 3: 6, 4: 8, 5: 8, 6: 6, 7: 5, 8: 7, 9: 6, 10: 3, 11: 7}, 'col6': {0: 0, 1: 1, 2: 1, 3: 1, 4: 1, 5: 0, 6: 1, 7: 1, 8: 0, 9: 0, 10: 0, 11: 1}, 'col7': {0: 6, 1: 8, 2: 7, 3: 2, 4: 6, 5: 5, 6: 7, 7: 8, 8: 7, 9: 8, 10: 6, 11: 2}, 'col8': {0: 6, 1: 4, 2: 2, 3: 7, 4: 3, 5: 7, 6: 4, 7: 5, 8: 1, 9: 5, 10: 3, 11: 7}}) df2 = df.melt(id_vars='index') g = sns.FacetGrid(data=df2, col='index', col_wrap=4) g.map(sns.barplot, 'variable', 'value', order=df2['variable'].unique()) plt.show()

enter image description here

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3 Comments

hey @Brendan this worked thanks a lot, but can you tell me how exactly which variable is doing what, especially 'variable' and 'value'. I tried degrees = 90 plt.xticks(rotation=degrees) but it only worked for the last graph. Thx in advance
@nasc 'variable' and 'value' are the default names from melt; you should likely rename them to something more sensible.
I did try something like that but got keyerror, is there a different way to change the names

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