TO START
import seaborn as sns
# If working on a notebook
%matplotlib inline
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DISTRIBUTION PLOTS
sns.distplot(df['col']) |
distribution plot |
- bin = x |
number of bins |
- kde = False |
remove the line |
sns.jointplot(x,y,df) |
plot 2 variables |
- kind = '' |
kind of plot* |
sns.pairplot(df) |
plot all vars combin |
- hue='categ var' |
distinguish per var |
- palette='' |
set a color palette |
sns.rugplot(df['col']) |
idea of distribution |
sns.kdeplot(df['col']) |
kde plot |
"kind=" E.g.: hex, reg, kde.
CATEGORICAL PLOTS
sns.barplot(x,y,df) |
bar plot |
- estimator=''* |
bar values |
sns.countplot(x,df) |
bars = count |
sns.boxplot(x,y,,df) |
box plot |
- hue='categ var' |
divide per var |
- palette='' |
set palette |
- orient='h' |
horiz. plot |
sns.violinplot(x,y,df) |
violin plot* |
- hue='categ var' |
divide per var |
- palette='' |
set palette |
sns.stripplot(x,y,df) |
bars = scatter |
- jitter = True |
add noise |
- hue='categ var' |
divide per var |
- palette='' |
set palette |
- split = True |
split by hue |
sns.swarmplot(x,y,df) |
swarm plot |
- hue='categ var' |
divide per var |
- palette='' |
set palette |
- split = True |
split by hue |
** You can alo combine more plots by calling them one after each other. |
sns.factorplot(x,y,df,kind)*
general categorical form of graph |
TIP: when you call a plot function, press
"shift + tab" to show the parameters needed.
estimator= can be, mean, std, or whatever function. It will display the bars or whatever you choose.
General form, kind=: e.g., point, bar, violin, etc.
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ON CATEGORICAL PLOTS...
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What is a violin plot? It has a similar role of a box and whisker plots. It shows the distribution of quantitative data across several levels of one (or more) categorical variables. The violin plot features a kernel density estimation of the underlying distribution.
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What is a strip plot? It will draw a scatterplot where one variable is categorical. It is also a good complement to a box or violin plot in cases where you want to show all observations along with some representation of the underlying distribution.
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What is a swarm plot It is similar to a stripplot(), but the points are adjusted (only along the categorical axis) so that they don’t overlap. This gives a better representation of the distribution of values, although it does not scale as well to large numbers of observations.
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MATRIX PLOTS
sns.heatmap(df.corr())* |
heat map plot |
- annot = True |
add actual values |
- cmap='' |
set a color palette |
- linecolor='' |
set borders |
- linewidths=x |
set border width |
sns.clustermap(matrix) |
hierarc. clustering |
- cmap='' |
set a color palette |
- standard_scale = 1 |
normalise data |
Heat map plot needs a correlation matrix, or more generally, a matrix. You can use the pivot_table(index,columns,values) function to convert a dataframe.
GRIDS
sns.pairplot(df) |
plot all vars combination |
- hue='categ var' |
divide per var |
- palette='' |
set palette |
g = sns.PairGrid(df) |
set (empty) axis of pairplot |
-g.map(plt.scatter) |
populate axis with some plot |
-g.map_diag(plt.hist) |
set diag plots |
-g.map_upper(plt.scatter) |
set upper plots |
-g.map_lower(sns.kdeplot) |
set lower plots |
g = sns.FacetGrid(df,c,r) |
empty axis |
-g = g.map(plt.hist, "c") |
populate axis histograms |
-..g.map(sns.distplot, "c") |
populate axis with distplots |
now some more complex stuff |
- hue='categ var' |
divide per var |
-g = g.map(plt.scatter, "c", "c").add_legend() |
g = sns.JointGrid(x,y,df)
general form of jointplot() |
g = g.plot(sns.regplot, sns.distplot)
join two plots |
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REGRESSION PLOTS
sns.lmplot(x,y,df) |
creat reg plot |
- hue='categ var' |
divide per var |
- palette='' |
set palette |
- markers='' * |
set mark shape |
- scatter_kws='dict' * |
set marker size |
sns.lmplot(x,y,df,col) |
create a grid plot |
sns.lmplot(x,y,df,row,col) |
X*X grid |
sns.lmplot(x,y,df,row,col,hue) |
X*X*X grid |
- aspect = x |
choose ratio |
- size = x |
set size |
markers='': e.g., o,v,etc.
scatter_kws='' e.g.: {'s':100}, it is a call to matplotlib. It will be hard to remember how to use these special cases, so no worries, you will have a look online.
STYLE and COLOR
sns.set_style('darkgrid') |
apply darkgrid style |
sns.set_style('ticks') |
apply ticks style |
sns.despine() |
remove borders |
sns.despine(left=True) |
remove left border |
plt.figure(figsize=(x,x)) |
choose fig size |
sns.set_context('talk') * |
set context |
sns.set_context
(font_scale) |
set font size |
.set_context(''): e.g.: paper, poster, talk, notebook, etc.
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