Update doc site URL

jsh9 · Apr 5, 2021 · 3e44b24 · 3e44b24
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diff --git a/README.md b/README.md
@@ -25,7 +25,7 @@ This is a Python module that contains some useful data visualization tools.
 
 In a command-line terminal, execute the following command:
 
-`pip install git+https://github.com/jsh9/python-plot-utilities@v0.6.8`
+`pip install git+https://github.com/jsh9/python-plot-utils@v0.6.8`
 
 ##### Note:
 
@@ -38,7 +38,7 @@ please follow this solution to fix the issue: https://stackoverflow.com/a/217899
 
 ## API documentations
 
-All API documentations are on: https://python-plot-utilities.readthedocs.io/en/stable/index.html.
+All API documentations are on: https://python-plot-utils.readthedocs.io/en/stable/index.html.
 
 
 ## Current functionalities
@@ -47,47 +47,47 @@ Current functionalities include (for full list, use `print(plot_utils.__doc__)`)
 
 #### 1. Visualizing one column of data
 
-- **Pie chart**: proportions of distinct values in an array, more convenient than matplotlib's `pie()` function [[doc](https://python-plot-utilities.readthedocs.io/en/stable/api_docs/pie_chart.html)], [[example](./examples/Pie_chart_example.ipynb)]
-- **Discrete histogram**: counts of distinct values in an array [[doc](https://python-plot-utilities.readthedocs.io/en/stable/api_docs/discrete_histogram.html)], [[example](./examples/Discrete_histogram_example.ipynb)]
+- **Pie chart**: proportions of distinct values in an array, more convenient than matplotlib's `pie()` function [[doc](https://python-plot-utils.readthedocs.io/en/stable/api_docs/pie_chart.html)], [[example](./examples/Pie_chart_example.ipynb)]
+- **Discrete histogram**: counts of distinct values in an array [[doc](https://python-plot-utils.readthedocs.io/en/stable/api_docs/discrete_histogram.html)], [[example](./examples/Discrete_histogram_example.ipynb)]
 
 #### 2. Visualizing two columns of data ([[example](./examples/Two_columns_of_data_example.ipynb)])
 
-- **"Bin-and-mean" plot**: for two continuous variables [[doc](https://python-plot-utilities.readthedocs.io/en/stable/api_docs/bin_and_mean.html)]
-- **Category mean**: for a categorical variable and a continuous variable [[doc](https://python-plot-utilities.readthedocs.io/en/stable/api_docs/category_means.html)]
-- **Positive rate**: for a categorical variable and a binary categorical variable [[doc](https://python-plot-utilities.readthedocs.io/en/stable/api_docs/positive_rate.html)]
-- **Contingency table**: for two categorical variables [[doc](https://python-plot-utilities.readthedocs.io/en/stable/api_docs/contingency_table.html)]
+- **"Bin-and-mean" plot**: for two continuous variables [[doc](https://python-plot-utils.readthedocs.io/en/stable/api_docs/bin_and_mean.html)]
+- **Category mean**: for a categorical variable and a continuous variable [[doc](https://python-plot-utils.readthedocs.io/en/stable/api_docs/category_means.html)]
+- **Positive rate**: for a categorical variable and a binary categorical variable [[doc](https://python-plot-utils.readthedocs.io/en/stable/api_docs/positive_rate.html)]
+- **Contingency table**: for two categorical variables [[doc](https://python-plot-utils.readthedocs.io/en/stable/api_docs/contingency_table.html)]
 
 #### 3. Visualizing multiple columns of data
 
-+ **3D histograms**: distributions of multiple variables [[doc](https://python-plot-utilities.readthedocs.io/en/stable/api_docs/3d_histograms.html)], [[example](./examples/3D_histograms_example.ipynb)]
-+ **Multiple histograms**: distribution of multiple variables [[doc](https://python-plot-utilities.readthedocs.io/en/stable/api_docs/hist_multi.html)], [[example](./examples/Violin_plot_and_hist_multi_example.ipynb)]
-+ **Violin plot**: distribution of multiple variables [[doc](https://python-plot-utilities.readthedocs.io/en/stable/api_docs/violin_plot.html)], [[example](./examples/Violin_plot_and_hist_multi_example.ipynb)]
-+ **Correlation matrix**: correlation between each columns of a dataset [[doc](https://python-plot-utilities.readthedocs.io/en/stable/api_docs/correlation_matrix.html)], [[example](./examples/Correlation_matrix_examples.ipynb)]
-    - and the one-to-one **scatter plots** for the variables within the dataset [[doc](https://python-plot-utilities.readthedocs.io/en/stable/api_docs/scatter_plot_two_cols.html)]
-+ **Count missing values**: how many missing values are there in each column of the dataset [[doc](https://python-plot-utilities.readthedocs.io/en/stable/api_docs/missing_values.html)], [[example](./examples/Missing_value_count_example.ipynb)]
++ **3D histograms**: distributions of multiple variables [[doc](https://python-plot-utils.readthedocs.io/en/stable/api_docs/3d_histograms.html)], [[example](./examples/3D_histograms_example.ipynb)]
++ **Multiple histograms**: distribution of multiple variables [[doc](https://python-plot-utils.readthedocs.io/en/stable/api_docs/hist_multi.html)], [[example](./examples/Violin_plot_and_hist_multi_example.ipynb)]
++ **Violin plot**: distribution of multiple variables [[doc](https://python-plot-utils.readthedocs.io/en/stable/api_docs/violin_plot.html)], [[example](./examples/Violin_plot_and_hist_multi_example.ipynb)]
++ **Correlation matrix**: correlation between each columns of a dataset [[doc](https://python-plot-utils.readthedocs.io/en/stable/api_docs/correlation_matrix.html)], [[example](./examples/Correlation_matrix_examples.ipynb)]
+    - and the one-to-one **scatter plots** for the variables within the dataset [[doc](https://python-plot-utils.readthedocs.io/en/stable/api_docs/scatter_plot_two_cols.html)]
++ **Count missing values**: how many missing values are there in each column of the dataset [[doc](https://python-plot-utils.readthedocs.io/en/stable/api_docs/missing_values.html)], [[example](./examples/Missing_value_count_example.ipynb)]
 
 
 #### 4. Map plotting
 
-+ **Choropleth map** (a.k.a., "heat map") of the United States, on both the state and county level [[doc](https://python-plot-utilities.readthedocs.io/en/stable/api_docs/choropleth_map.html)], [[example](./examples/Choropleth_map_example.ipynb)]
++ **Choropleth map** (a.k.a., "heat map") of the United States, on both the state and county level [[doc](https://python-plot-utils.readthedocs.io/en/stable/api_docs/choropleth_map.html)], [[example](./examples/Choropleth_map_example.ipynb)]
 
 
 #### 5. Time series plotting
 
-- **Plot single time series**: [[doc](https://python-plot-utilities.readthedocs.io/en/stable/api_docs/plot_time_series.html)], [[example](./examples/Plot_time_series_example.ipynb)]
-- **Plot multiple time series**: [[doc](https://python-plot-utilities.readthedocs.io/en/stable/api_docs/plot_multiple_timeseries.html)], [[example](./examples/Plot_time_series_example.ipynb)]
-- **Fill time series with error bounds**: [[doc](https://python-plot-utilities.readthedocs.io/en/stable/api_docs/fill_timeseries.html)], [[example](./examples/Plot_time_series_example.ipynb)]
+- **Plot single time series**: [[doc](https://python-plot-utils.readthedocs.io/en/stable/api_docs/plot_time_series.html)], [[example](./examples/Plot_time_series_example.ipynb)]
+- **Plot multiple time series**: [[doc](https://python-plot-utils.readthedocs.io/en/stable/api_docs/plot_multiple_timeseries.html)], [[example](./examples/Plot_time_series_example.ipynb)]
+- **Fill time series with error bounds**: [[doc](https://python-plot-utils.readthedocs.io/en/stable/api_docs/fill_timeseries.html)], [[example](./examples/Plot_time_series_example.ipynb)]
 
 #### 6. Miscellaneous
 
-+ A **get_colors()** function that conveniently queries different color palettes [[doc](https://python-plot-utilities.readthedocs.io/en/stable/api_docs/get_colors.html)], [[example](./examples/Color_and_linespec_examples.ipynb)]
-+ A **get_linespecs()** function that generates distinct color/linestyle/linewidth combinations for plotting many lines together [[doc](https://python-plot-utilities.readthedocs.io/en/stable/api_docs/get_linespecs.html)], [[example](./examples/Color_and_linespec_examples.ipynb)]
-+ Two helper classes: **Color** and **Multiple_Colors**, which make querying and displaying colors more easily  [[doc](https://python-plot-utilities.readthedocs.io/en/stable/api_docs/color_classes.html)], [[example](./examples/Color_and_linespec_examples.ipynb)]
-+ **Plotting with error bounds**, which displays error bounds as shaded areas [[doc](https://python-plot-utilities.readthedocs.io/en/stable/api_docs/plot_with_bounds.html)], [[example](./examples/Plot_with_error_bounds_example.ipynb)]
-+ **trim_img()**, which trims the white margins of the specified image file(s) [[doc](https://python-plot-utilities.readthedocs.io/en/stable/api_docs/trim_image.html)]
-+ **pad_img()**, which pads image(s) with margins so that they meet a specified aspect ratio [[doc](https://python-plot-utilities.readthedocs.io/en/stable/api_docs/pad_image.html)]
-+ **plot_ranking()**, which ranks a series of values and shows them as a bar chart [[doc](https://python-plot-utilities.readthedocs.io/en/stable/api_docs/plot_ranking.html)], [[example](./examples/Plot_ranking_example.ipynb)]
-+ **visualize_cv_scores()**, which visualizes cross-validation training/evaluation scores [[doc](https://python-plot-utilities.readthedocs.io/en/stable/api_docs/visualize_cv_scores.html)], [[example](./examples/Visualize_cross_validation_results.ipynb)]
++ A **get_colors()** function that conveniently queries different color palettes [[doc](https://python-plot-utils.readthedocs.io/en/stable/api_docs/get_colors.html)], [[example](./examples/Color_and_linespec_examples.ipynb)]
++ A **get_linespecs()** function that generates distinct color/linestyle/linewidth combinations for plotting many lines together [[doc](https://python-plot-utils.readthedocs.io/en/stable/api_docs/get_linespecs.html)], [[example](./examples/Color_and_linespec_examples.ipynb)]
++ Two helper classes: **Color** and **Multiple_Colors**, which make querying and displaying colors more easily  [[doc](https://python-plot-utils.readthedocs.io/en/stable/api_docs/color_classes.html)], [[example](./examples/Color_and_linespec_examples.ipynb)]
++ **Plotting with error bounds**, which displays error bounds as shaded areas [[doc](https://python-plot-utils.readthedocs.io/en/stable/api_docs/plot_with_bounds.html)], [[example](./examples/Plot_with_error_bounds_example.ipynb)]
++ **trim_img()**, which trims the white margins of the specified image file(s) [[doc](https://python-plot-utils.readthedocs.io/en/stable/api_docs/trim_image.html)]
++ **pad_img()**, which pads image(s) with margins so that they meet a specified aspect ratio [[doc](https://python-plot-utils.readthedocs.io/en/stable/api_docs/pad_image.html)]
++ **plot_ranking()**, which ranks a series of values and shows them as a bar chart [[doc](https://python-plot-utils.readthedocs.io/en/stable/api_docs/plot_ranking.html)], [[example](./examples/Plot_ranking_example.ipynb)]
++ **visualize_cv_scores()**, which visualizes cross-validation training/evaluation scores [[doc](https://python-plot-utils.readthedocs.io/en/stable/api_docs/visualize_cv_scores.html)], [[example](./examples/Visualize_cross_validation_results.ipynb)]
 
 
 ## Gallery
@@ -104,9 +104,9 @@ pu.piechart(titanic['survived'], title='Suvived')
 pu.discrete_histogram(titanic['pclass'], xlabel='Passenger ticket class')
 ```
 
-Piechart: [[doc](https://python-plot-utilities.readthedocs.io/en/stable/api_docs/pie_chart.html)], [[example](./examples/Pie_chart_example.ipynb)]
+Piechart: [[doc](https://python-plot-utils.readthedocs.io/en/stable/api_docs/pie_chart.html)], [[example](./examples/Pie_chart_example.ipynb)]
 
-Discrete histogram: [[doc](https://python-plot-utilities.readthedocs.io/en/stable/api_docs/discrete_histogram.html)], [[example](./examples/Discrete_histogram_example.ipynb)]
+Discrete histogram: [[doc](https://python-plot-utils.readthedocs.io/en/stable/api_docs/discrete_histogram.html)], [[example](./examples/Discrete_histogram_example.ipynb)]
 
 ![](./examples/gallery/piechart_and_discrete_hist.png)
 
@@ -129,10 +129,10 @@ pu.positive_rate(titanic['ticket_class'], titanic['survived'], figsize=(5,2))
 pu.contingency_table(titanic['ticket_class'], titanic['embarked'], dropna=True, rot=0)
 ```
 
-[["bin-and-mean" doc](https://python-plot-utilities.readthedocs.io/en/stable/api_docs/bin_and_mean.html)]
-[["category means" doc](https://python-plot-utilities.readthedocs.io/en/stable/api_docs/category_means.html)]
-[["positive rate" doc](https://python-plot-utilities.readthedocs.io/en/stable/api_docs/positive_rate.html)]
-[["contingency table" doc](https://python-plot-utilities.readthedocs.io/en/stable/api_docs/contingency_table.html)]
+[["bin-and-mean" doc](https://python-plot-utils.readthedocs.io/en/stable/api_docs/bin_and_mean.html)]
+[["category means" doc](https://python-plot-utils.readthedocs.io/en/stable/api_docs/category_means.html)]
+[["positive rate" doc](https://python-plot-utils.readthedocs.io/en/stable/api_docs/positive_rate.html)]
+[["contingency table" doc](https://python-plot-utils.readthedocs.io/en/stable/api_docs/contingency_table.html)]
 [[All examples](./examples/Two_columns_of_data_example.ipynb)]
 
 ![](./examples/gallery/two_variables.png)
@@ -150,7 +150,7 @@ iris = pd.read_csv('./examples/datasets/iris.csv')
 pu.histogram3d(iris[['petal_width', 'petal_length', 'sepal_width', 'sepal_length']])
 ```
 
-[[doc](https://python-plot-utilities.readthedocs.io/en/stable/api_docs/3d_histograms.html)], [[example](./examples/3D_histograms_example.ipynb)]
+[[doc](https://python-plot-utils.readthedocs.io/en/stable/api_docs/3d_histograms.html)], [[example](./examples/3D_histograms_example.ipynb)]
 
 ![histogram_3d](./examples/gallery/histogram_3d.png)
 
@@ -163,7 +163,7 @@ Another useful tool to compare multiple distributions:
 pu.hist_multi(iris[['sepal_length', 'sepal_width', 'petal_length', 'petal_width']]);
 ```
 
-[[doc](https://python-plot-utilities.readthedocs.io/en/stable/api_docs/hist_multi.html)], [[example](./examples/Violin_plot_example.ipynb)]
+[[doc](https://python-plot-utils.readthedocs.io/en/stable/api_docs/hist_multi.html)], [[example](./examples/Violin_plot_example.ipynb)]
 
 ![](./examples/gallery/multi_histogram.png)
 
@@ -175,7 +175,7 @@ To compare multiple distributions:
 pu.violin_plot(iris[['petal_width', 'petal_length', 'sepal_width', 'sepal_length']])
 ```
 
-[[doc](https://python-plot-utilities.readthedocs.io/en/stable/api_docs/violin_plot.html)], [[example](./examples/Violin_plot_example.ipynb)]
+[[doc](https://python-plot-utils.readthedocs.io/en/stable/api_docs/violin_plot.html)], [[example](./examples/Violin_plot_example.ipynb)]
 
 ![](./examples/gallery/violin_plots.png)
 
@@ -190,7 +190,7 @@ pu.plot_correlation(iris, scatter_plots=True)
 
 The first figure shows the correlation (or "sample covariance")  between each column. The second figure shows the scatter plots between each pair of columns.
 
-[[doc](https://python-plot-utilities.readthedocs.io/en/stable/api_docs/correlation_matrix.html)], [[example](./examples/Correlation_matrix_examples.ipynb)]
+[[doc](https://python-plot-utils.readthedocs.io/en/stable/api_docs/correlation_matrix.html)], [[example](./examples/Correlation_matrix_examples.ipynb)]
 
 ![](./examples/gallery/correlation_matrix.png)
 
@@ -207,7 +207,7 @@ titanic = pd.read_csv('./examples/datasets/titanic3.csv')
 pu.missing_value_counts(titanic)
 ```
 
-[[doc](https://python-plot-utilities.readthedocs.io/en/stable/api_docs/missing_values.html)], [[example](./examples/Missing_value_count_example.ipynb)]
+[[doc](https://python-plot-utils.readthedocs.io/en/stable/api_docs/missing_values.html)], [[example](./examples/Missing_value_count_example.ipynb)]
 
 Each bar corresponds to a column in `titanic`, and the numbers atop are the missing data counts for the corresponding column.
 
@@ -223,7 +223,7 @@ Each bar corresponds to a column in `titanic`, and the numbers atop are the miss
 pu.choropleth_map_state(state_level_data)  # `state_level_data`: dict, pandas.DataFrame, or pandas.Series
 ```
 
-[[doc](https://python-plot-utilities.readthedocs.io/en/stable/api_docs/choropleth_map.html)], [[example](./examples/Choropleth_map_example.ipynb)]
+[[doc](https://python-plot-utils.readthedocs.io/en/stable/api_docs/choropleth_map.html)], [[example](./examples/Choropleth_map_example.ipynb)]
 
 ![choropleth_map_state](./examples/gallery/choropleth_map_state.png)
 
@@ -233,7 +233,7 @@ pu.choropleth_map_state(state_level_data)  # `state_level_data`: dict, pandas.Da
 pu.choropleth_map_county(county_level_data)  # `county_level_data`: dict, pandas.DataFrame, or pandas.Series
 ```
 
-[[doc](https://python-plot-utilities.readthedocs.io/en/stable/api_docs/choropleth_map.html)], [[example](./examples/Choropleth_map_example.ipynb)]
+[[doc](https://python-plot-utils.readthedocs.io/en/stable/api_docs/choropleth_map.html)], [[example](./examples/Choropleth_map_example.ipynb)]
 
 ![choropleth_map_county](./examples/gallery/choropleth_map_county.png)
 
@@ -248,7 +248,7 @@ df = pd.read_csv('./examples/datasets/Unemployment_rate_1976-2017.csv', index_co
 pu.plot_timeseries(df['CA'], ylabel='Unit: %', title='Unemployment rate, California')
 ```
 
-[[doc](https://python-plot-utilities.readthedocs.io/en/stable/api_docs/plot_time_series.html)], [[example](./examples/Plot_time_series_example.ipynb)]
+[[doc](https://python-plot-utils.readthedocs.io/en/stable/api_docs/plot_time_series.html)], [[example](./examples/Plot_time_series_example.ipynb)]
 
 ![](./examples/gallery/time_series_single.png)
 
@@ -258,7 +258,7 @@ pu.plot_timeseries(df['CA'], ylabel='Unit: %', title='Unemployment rate, Califor
 pu.plot_multiple_timeseries(df, ylabel='Unemployment rate [%]', ncol_legend=10)
 ```
 
-[[doc](https://python-plot-utilities.readthedocs.io/en/stable/api_docs/plot_multiple_timeseries.html)], [[example](./examples/Plot_time_series_example.ipynb)]
+[[doc](https://python-plot-utils.readthedocs.io/en/stable/api_docs/plot_multiple_timeseries.html)], [[example](./examples/Plot_time_series_example.ipynb)]
 
 ![](./examples/gallery/time_series.png)
 
@@ -275,7 +275,7 @@ colors = pu.get_colors(color_scheme='tab10', N=10)  # `colors`: a list containin
 pu.Multiple_Colors(colors).show()  # show colors as a palette
 ```
 
-[[doc](https://python-plot-utilities.readthedocs.io/en/stable/api_docs/get_colors.html)], [[example](./examples/Color_and_linespec_examples.ipynb)]
+[[doc](https://python-plot-utils.readthedocs.io/en/stable/api_docs/get_colors.html)], [[example](./examples/Color_and_linespec_examples.ipynb)]
 
 ![](./examples/gallery/get_colors.png)
 
@@ -288,9 +288,9 @@ line_specs = pu.get_linespecs(color_scheme='bw',range_linewidth=[3,8],priority='
 pu.linespecs_demo(line_specs)
 ```
 
-[[doc](https://python-plot-utilities.readthedocs.io/en/stable/api_docs/get_linespecs.html)], [[example](./examples/Color_and_linespec_examples.ipynb)]
+[[doc](https://python-plot-utils.readthedocs.io/en/stable/api_docs/get_linespecs.html)], [[example](./examples/Color_and_linespec_examples.ipynb)]
 
-[[`get_linespecs()` doc](https://python-plot-utilities.readthedocs.io/en/stable/api_docs/linespecs_demo.html)]
+[[`get_linespecs()` doc](https://python-plot-utils.readthedocs.io/en/stable/api_docs/linespecs_demo.html)]
 
 ![](./examples/gallery/get_linespecs.png)
 
@@ -302,7 +302,7 @@ Plots data and error bounds on the same graph.
 pu.plot_with_error_bounds(data, upper_bound, lower_bound)
 ```
 
-[[doc](https://python-plot-utilities.readthedocs.io/en/stable/api_docs/plot_with_bounds.html)], [[example](./examples/Plot_with_error_bounds_example.ipynb)]
+[[doc](https://python-plot-utils.readthedocs.io/en/stable/api_docs/plot_with_bounds.html)], [[example](./examples/Plot_with_error_bounds_example.ipynb)]
 
 ![](./examples/gallery/error_bounds.png)
 
@@ -311,7 +311,7 @@ pu.plot_with_error_bounds(data, upper_bound, lower_bound)
 ```python
 pu.visualize_cv_scores(n_folds=4, cv_scores=[0.675, 0.702, 0.689, 0.653], holdout_score=0.6745);
 ```
-[[doc](https://python-plot-utilities.readthedocs.io/en/stable/api_docs/visualize_cv_scores.html)], [[example](./examples/Visualize_cross_validation_results.ipynb)]
+[[doc](https://python-plot-utils.readthedocs.io/en/stable/api_docs/visualize_cv_scores.html)], [[example](./examples/Visualize_cross_validation_results.ipynb)]
 
 ![](./examples/gallery/visualize_cv_scores.png)