separating seaborn barplot with two colors

and send it to your bar plot:

g = sns.barplot(
   x = words,
   y = coefs,
   hue = colors, # Here I am!
   palette = sns.color_palette() # Default palette is far better
   for it
)

Based on @vurmux answer, using hue will decenter your bars, which is noticeable in smaller plots. You can alternatively just build a list of colors and pass it to the palette argument, like this

colors = ['g'
   if c >= 0
   else 'r'
   for c in coefs
]
g = sns.barplot(
   x = words,
   y = coefs,
   palette = colors
)

Suggestion : 2

I have the following dataset:,Seaborn's color schemes are repeating. anycodings_python For example, let's use this palette:,and I would like to make a barplot with two anycodings_python colors, one for negative and one for anycodings_python positive values of the coefficient. ,But even here I cannot trully separate them, anycodings_python it only makes a scale of colors.

I have the following dataset:

words = ['upvoted', 'upvote', 'f***', 'reimer', 'feminists', 'censorship',
   'wet', '0001f914', 'turtle', '0001f602', 'vegans', 'thumbnail',
   'lobby', 'mods', 'removed', 'bitches', 'saffron', 'broadband',
   'hitler', 'ass', 'deleted', 'u', 'tits', 'cheating', 'antifa',
   'iÃƒÂ¢', 'ÃƒÂ¢', 'itÃƒÂ¢', 'donÃƒÂ¢', 'edit', 'thatÃƒÂ¢', 'isnÃƒÂ¢', 'doesnÃƒÂ¢',
   'didnÃƒÂ¢', 'canÃƒÂ¢', 'youÃƒÂ¢', 'theyÃƒÂ¢', 'eli5', 'weÃƒÂ¢', 'arenÃƒÂ¢', 'thereÃƒÂ¢',
   'hi', 'wouldnÃƒÂ¢', 'Ã‚Â½ÃƒÂ¯', 'whatÃƒÂ¢', 'ÃƒÂ¯', 'Ã‚Â½', 'wasnÃƒÂ¢', 'wonÃƒÂ¢',
   'eclipse'
]

coefs = [1.00157191, 0.95931338, 0.92066619, 0.86347946, 0.83977936,
   0.83912351, 0.83482245, 0.8148754, 0.79982483, 0.79402501,
   0.7687297, 0.76765479, 0.76096785, 0.75893433, 0.75177131,
   0.74486391, 0.73244163, 0.71302245, 0.70449932, 0.70346844,
   0.69737316, 0.67902944, 0.6746799, 0.67338842, 0.6678747,
   -2.83723585, -2.82874502, -2.59032368, -2.52985115, -2.1811188,
   -1.87094025, -1.66191757, -1.64283967, -1.62282287, -1.61855926,
   -1.55062817, -1.54397537, -1.39775882, -1.3492324, -1.30638486,
   -1.29859752, -1.14761071, -1.0673105, -1.06272808, -1.02998239,
   -0.96635257, -0.94262438, -0.91244845, -0.90765028, -0.87274524
]

My efforts have lead me to:

sns.set(rc = {
   'figure.figsize': (20, 10)
})
sns.set(font_scale = 1.5)
g = sns.barplot(words, coefs, palette = sns.diverging_palette(220, 20, n = 50, center = "dark"))
plt.xticks(rotation = 90)
plt.show()

and send it to your bar plot:

g = sns.barplot(
   x = words,
   y = coefs,
   hue = colors, # Here I am!
   palette = sns.color_palette() # Default palette is far better
   for it
)

Based on @vurmux answer, using hue will anycodings_python decenter your bars, which is noticeable anycodings_python in smaller plots. You can alternatively anycodings_python just build a list of colors and pass it anycodings_python to the palette argument, like this

colors = ['g'
   if c >= 0
   else 'r'
   for c in coefs
]
g = sns.barplot(
   x = words,
   y = coefs,
   palette = colors
)

Suggestion : 3

Proportion of the original saturation to draw colors at. Large patches often look better with slightly desaturated colors, but set this to 1 if you want the plot colors to perfectly match the input color spec.,Colors to use for the different levels of the hue variable. Should be something that can be interpreted by color_palette(), or a dictionary mapping hue levels to matplotlib colors.,A bar plot represents an estimate of central tendency for a numeric variable with the height of each rectangle and provides some indication of the uncertainty around that estimate using error bars. Bar plots include 0 in the quantitative axis range, and they are a good choice when 0 is a meaningful value for the quantitative variable, and you want to make comparisons against it.,Orientation of the plot (vertical or horizontal). This is usually inferred based on the type of the input variables, but it can be used to resolve ambiguity when both x and y are numeric or when plotting wide-form data.

>>>
import seaborn as sns
   >>>
   sns.set_theme(style = "whitegrid") >>>
   tips = sns.load_dataset("tips") >>>
   ax = sns.barplot(x = "day", y = "total_bill", data = tips)

>>> ax = sns.barplot(x = "day", y = "total_bill", hue = "sex", data = tips)

>>> ax = sns.barplot(x = "tip", y = "day", data = tips)

>>> ax = sns.barplot(x = "time", y = "tip", data = tips,
   ...order = ["Dinner", "Lunch"])

>>> from numpy
import median
   >>>
   ax = sns.barplot(x = "day", y = "tip", data = tips, estimator = median)

>>> ax = sns.barplot(x = "day", y = "tip", data = tips, ci = 68)