Update scale.py docstrings. #10
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| from lets_plot import * | ||
| LetsPlot.setup_html() | ||
| x = np.arange(10) | ||
| c = np.where(x < 5, '0', '1') |
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When you use discrete string variable, maybe it is better to prefer values like 'a' and 'b' or 'x' and 'y' (or whatever symbols, but not digits).
| Name of built-in transformation. ('identity', 'log10', 'sqrt', 'reverse') | ||
| format : string | ||
| trans : str | ||
| Name of built-in transformation ('identity', 'log10', 'sqrt', 'reverse'). |
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If it is all possible values, we could use the following construction:
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trans : {'identity', 'log10', 'sqrt', 'reverse'}
Name of built-in transformation.
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| @@ -117,26 +123,25 @@ def scale_x_continuous(name=None, breaks=None, labels=None, limits=None, expand= | |||
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In other places we used the following:
For more info see the `formatting reference <https://jetbrains.github.io/lets-plot-docs/pages/features/formats.html>`_.
| from lets_plot import * | ||
| LetsPlot.setup_html() | ||
| np.random.seed(42) | ||
| n = 100 |
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This variable used only once. You could reject it.
| from lets_plot import * | ||
| LetsPlot.setup_html() | ||
| np.random.seed(42) | ||
| n = 1000 |
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Again, you could drop this extra variable.
| LetsPlot.setup_html() | ||
| from datetime import datetime | ||
| economics_url = 'https://vincentarelbundock.github.io/Rdatasets/csv/ggplot2/economics.csv' | ||
| economics = pd.read_csv(economics_url) |
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We prefer don't use data that comes through the Internet. At least it should be another example that works offline.
| economics = pd.read_csv(economics_url) | ||
| economics['date'] = pd.to_datetime(economics['date']) | ||
| start = datetime(2000, 1, 1) | ||
| economics = economics.loc[economics['date'] >= start] |
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This line added because of issue #346. Example should be updated later after fixing the error or right now for preventing this extra moves.
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| Note | ||
| ----- | ||
| Continuous position scales. | ||
| """ |
| data['y'] = np.append(np.random.normal(0, 1, 1000), np.random.normal(3, 1, 500)) | ||
| ggplot(data, aes('x', 'y')) + geom_point(aes(alpha='..density..'),\\ | ||
| stat='density2d', contour=False, n=30) + \\ | ||
| scale_alpha(range=[0.05, 0.9]) |
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Without loss of usefulness it may be simplified up to
import numpy as np
from lets_plot import *
LetsPlot.setup_html()
np.random.seed(100)
x = np.random.normal(0, 1, 1000)
y = np.random.normal(0, 1, 1000)
ggplot({'x': x, 'y': y}, aes('x', 'y')) + \\
geom_point(aes(alpha='..density..'), stat='density2d', contour=False, n=30) + \\
scale_alpha(range=[.01, .99])| from lets_plot import * | ||
| LetsPlot.setup_html() | ||
| np.random.seed(100) | ||
| N = 50 |
Update scale.py docstrings.