Palette says it all !

On my journey of learning python and performing exploratory data analysis. I have come to realised that performing EDA and creatively visualising the data goes hand-in hand and when we say creatively visualising it includes :
- Nomenclature of Graphs
- No overlapping of attributes on the axis
- Choosing the correct colour palettes.

Somewhere themes, makes the graphs look more pleasing to the eyes and that why I believe both Matplotlib and Seaborn library has various palettes. I am going to share those which are my personal favourite which I always include while performing the EDA on any dataset.

For Charts

1. palette = ‘cubehelix’
   code = df[‘ ’].plot.pie(colors = sns.color_palette(‘cubehelix’)

2. palette = ‘Spectral’
Code = sns.barplot(x=‘ ’ y=‘ ’, data = , hue = ‘ ’, palette=‘Spectral’)

3. palette = ‘vlag’
Code = sns.barplot(x=‘ ’ y=‘ ’, data = , hue = ‘ ’, palette=‘vlag’)

For Heatmaps :

1. cmap=‘icefire’
   Code = sns.heatmap(df.corr(),annot=True, cmap=‘icefire’ )

2. cmap = ‘coolwarm’
Code = sns.color_palette(“coolwarm”, as_cmap=True)

3. cmap = ‘seagreen’
Code = sns.light_palette(“seagreen”, as_cmap=True)

Well, these are my ‘go to palette’ when it comes EDA, what are yours ?

PS — Always follow a uniform theme throughout the data set, it makes the analysis look more organised and streamlined.

Thank- you for reading it till the end. Feel free to add you suggestion.