Coming soon!
+ + + + + +Coming soon!
+ + + + + + + + +Coming soon!
+ + + + + +Coming soon!
+ + + + + + + + +Coming soon!
+ + + + + +Coming soon!
+ + + + + + + + +Coming soon!
+ + + + + +Coming soon!
+ + + + + + + + +Make sure you enable dx as a pandas plotting backend first.
+
dx.bar(
+ df,
+ x='keyword_column',
+ y='integer_column',
+ y2='float_column',
+ y2_style='dot',
+ horizontal=True,
+ bar_width='index',
+ group_other=True,
+ column_sort_order="desc",
+ column_sort_type="string",
+ pro_bar_mode="combined",
+ combination_mode="max",
+ show_bar_labels=True,
+)
+
+Make sure you enable dx as a pandas plotting backend first.
df.plot.bar(
+ x='keyword_column',
+ y='integer_column',
+ y2='float_column',
+ y2_style='dot',
+ horizontal=True,
+ bar_width='index',
+ group_other=True,
+ column_sort_order="desc",
+ column_sort_type="string",
+ pro_bar_mode="combined",
+ combination_mode="max",
+ show_bar_labels=True,
+)
+
+src.dx.plotting.dex.basic_charts.bar(df, x, y, y2=None, y2_style='bar', horizontal=False, bar_width=None, group_other=False, column_sort_order='asc', column_sort_type='string', pro_bar_mode='combined', combination_mode='avg', show_bar_labels=False, return_view=False, **kwargs)
+
+Generates a DEX bar plot from the given DataFrame.
+ + + +Parameters:
+| Name | +Type | +Description | +Default | +
|---|---|---|---|
df |
+ + | +
+
+
+ The DataFrame to plot. + |
+ + required + | +
x |
+
+ str
+ |
+
+
+
+ The column to use for the x-axis. + |
+ + required + | +
y |
+
+ str
+ |
+
+
+
+ The column(s) to use for the primary y-axis. + |
+ + required + | +
y2 |
+
+ Optional[str]
+ |
+
+
+
+ The column to use for the secondary y-axis. + |
+
+ None
+ |
+
y2_style |
+
+ DEXSecondMetricstyle
+ |
+
+
+
+ The style to use for the secondary y-axis. ( |
+
+ 'bar'
+ |
+
horizontal |
+
+ bool
+ |
+
+
+
+ Whether to plot the bars horizontally. + |
+
+ False
+ |
+
bar_width |
+
+ Optional[str]
+ |
+
+
+
+ The column to use for the bar width. + |
+
+ None
+ |
+
group_other |
+
+ bool
+ |
+
+
+
+ Whether to group the remaining columns into an "Other" category. + |
+
+ False
+ |
+
column_sort_order |
+
+ DEXSortColumnsByOrder
+ |
+
+
+
+ The order to sort the columns by ( |
+
+ 'asc'
+ |
+
column_sort_type |
+
+ DEXSortColumnsByType
+ |
+
+
+
+ The type of sorting to use. ( |
+
+ 'string'
+ |
+
pro_bar_mode |
+
+ DEXProBarModeType
+ |
+
+
+
+ The bar mode to use ( |
+
+ 'combined'
+ |
+
combination_mode |
+
+ DEXCombinationMode
+ |
+
+
+
+ The combination mode to use ( |
+
+ 'avg'
+ |
+
show_bar_labels |
+
+ bool
+ |
+
+
+
+ Whether to show the bar values as labels. + |
+
+ False
+ |
+
return_view |
+
+ bool
+ |
+
+
+
+ Whether to return a |
+
+ False
+ |
+
**kwargs |
+ + | +
+
+
+ Additional keyword arguments to pass to the view metadata. + |
+
+ {}
+ |
+
src/dx/plotting/dex/basic_charts.py32 + 33 + 34 + 35 + 36 + 37 + 38 + 39 + 40 + 41 + 42 + 43 + 44 + 45 + 46 + 47 + 48 + 49 + 50 + 51 + 52 + 53 + 54 + 55 + 56 + 57 + 58 + 59 + 60 + 61 + 62 + 63 + 64 + 65 + 66 + 67 + 68 + 69 + 70 + 71 + 72 + 73 + 74 + 75 + 76 + 77 + 78 + 79 + 80 + 81 + 82 + 83 + 84 + 85 + 86 + 87 + 88 + 89 + 90 + 91 + 92 + 93 + 94 + 95 + 96 + 97 + 98 + 99 +100 +101 +102 +103 +104 +105 +106 +107 +108 +109 +110 +111 +112 +113 +114 +115 | |
Coming soon!
+ + + + + +Coming soon!
+ + + + + + + + +Coming soon!
+ + + + + +Coming soon!
+ + + + + + + + +Coming soon!
+ + + + + +Coming soon!
+ + + + + + + + +Coming soon!
+ + + + + +Coming soon!
+ + + + + + + + +Coming soon!
+ + + + + +Coming soon!
+ + + + + + + + +Coming soon!
+ + + + + +Coming soon!
+ + + + + + + + +Coming soon!
+ + + + + +Coming soon!
+ + + + + + + + +Just like the plotting functions, generating dashboards with dx is very experimental and prone to change.
If you create charts using dx functions, you may want to combine them into a single view or dashboard. This can be done with dashboard().
Similar to the chart functions, dashboard() mainly requires a pandas DataFrame, as well as a list of views in a matrix-like orientation. (Each item in the list is treated as a row, and each row can be a list of views to specify column positioning.)
Here's a quick example where we make a dashboard using two rows -- the top row will have scatter and bar charts, and the bottom will be our default grid view:
+ +
+Using chart functions, you can specify return_view=True and pass the resulting DEXView object into a dashboard.
+
custom_bar_chart = dx.bar(
+ df,
+ x='keyword_column',
+ y='integer_column',
+ column_sort_order='desc',
+ column_sort_type='string',
+ show_bar_labels=True,
+ return_view=True,
+)
+
+simple_tilemap = dx.tilemap(
+ df,
+ lat='index',
+ lon='integer_column',
+ return_view=True,
+)
+
+dx.dashboard(
+ df,
+ views=[
+ ["parallel_coordinates", custom_bar_chart],
+ [simple_tilemap, 'pie', 'grid']
+ ]
+)
+
+If you want to provide some additional arguments, instead of passing a string to indicate the chart type, you can pass a dictionary with {"chart_mode": CHART TYPE, **extra_kwargs}.
+
custom_chart = {
+ "chart_mode": "hexbin",
+ "decoration": {
+ "title": "look at this sweet hexbin"
+ },
+}
+
+dx.dashboard(
+ df,
+ views=[
+ [custom_chart, "force_directed_network"],
+ ["ridgeline"]
+ ]
+)
+
+
+
+
+src.dx.plotting.dashboards.dashboard(df, views, **kwargs)
+
+Creates and renders a DEX dashboard from a list of views.
+ + + +Parameters:
+| Name | +Type | +Description | +Default | +
|---|---|---|---|
df |
+
+ DataFrame
+ |
+
+
+
+ The dataframe to be rendered. + |
+ + required + | +
views |
+
+ List[Union[str, dict, list, DEXView]]
+ |
+
+
+
+ A list of views to be created and rendered in the dashboard. +By default, each item in the list will be treated as a row item. +Each item in the list can be another nested list of views to +determine column positioning within the dashboard view. + |
+ + required + | +
src/dx/plotting/dashboards.py14 +15 +16 +17 +18 +19 +20 +21 +22 +23 +24 +25 +26 +27 +28 +29 +30 +31 +32 +33 +34 +35 +36 +37 +38 +39 +40 +41 +42 +43 +44 +45 +46 +47 +48 +49 +50 +51 +52 +53 +54 +55 +56 +57 +58 +59 +60 +61 +62 +63 +64 +65 +66 +67 +68 +69 +70 +71 +72 +73 +74 +75 +76 +77 +78 +79 +80 +81 +82 +83 +84 +85 +86 +87 +88 +89 +90 +91 +92 +93 | |
Coming soon!
+ + + + + +Coming soon!
+ + + + + + + + +Coming soon!
+ + + + + +Coming soon!
+ + + + + + + + +Coming soon!
+ + + + + +Coming soon!
+ + + + + + + + +Coming soon!
+ + + + + +Coming soon!
+ + + + + + + + +Coming soon!
+ + + + + +Coming soon!
+ + + + + + + + +Coming soon!
+ + + + + +Coming soon!
+ + + + + + + + +Coming soon!
+ + + + + +Coming soon!
+ + + + + + + + +Coming soon!
+ + + + + +Coming soon!
+ + + + + + + + +Coming soon!
+ + + + + +Coming soon!
+ + + + + + + + +Coming soon!
+ + + + + +Coming soon!
+ + + + + + + + +Coming soon!
+ + + + + +Coming soon!
+ + + + + + + + +Coming soon!
+ + + + + +Coming soon!
+ + + + + + + + +Coming soon!
+ + + + + +Coming soon!
+ + + + + + + + +Coming soon!
+ + + + + +Coming soon!
+ + + + + + + + +Coming soon!
+ + + + + +Coming soon!
+ + + + + + + + +Coming soon!
+ + + + + +Coming soon!
+ + + + + + + + +Make sure you enable dx as a pandas plotting backend first.
+You may need to use a larger dataset to see the changes here. For these examples, we used dx.random_dataframe(5000).
dx.line(
+ df,
+ x='datetime_column',
+ y='integer_column',
+ line_type="cumulative",
+ split_by="keyword_column",
+ multi_axis=True,
+ smoothing="hourly",
+ use_count=True,
+ bounding_type="relative",
+ zero_baseline=True,
+ combination_mode="min",
+)
+
+Make sure you enable dx as a pandas plotting backend first.
df.plot.line(
+ x='datetime_column',
+ y='integer_column',
+ line_type="cumulative",
+ split_by="keyword_column",
+ multi_axis=True,
+ smoothing="hourly",
+ use_count=True,
+ bounding_type="relative",
+ zero_baseline=True,
+ combination_mode="min",
+)
+
+src.dx.plotting.dex.basic_charts.line(df, x, y, line_type='line', split_by=None, multi_axis=False, smoothing=None, use_count=False, bounding_type='absolute', zero_baseline=False, combination_mode='avg', return_view=False, **kwargs)
+
+Generates a DEX line plot from the given DataFrame.
+ + + +Parameters:
+| Name | +Type | +Description | +Default | +
|---|---|---|---|
df |
+ + | +
+
+
+ The DataFrame to plot. + |
+ + required + | +
x |
+
+ str
+ |
+
+
+
+ The column to use for the x-axis. + |
+ + required + | +
y |
+
+ Union[List[str], str]
+ |
+
+
+
+ The column(s) to use for the y-axis. + |
+ + required + | +
line_type |
+
+ DEXLineType
+ |
+
+
+
+ The line type to use:
+ - |
+
+ 'line'
+ |
+
split_by |
+
+ Optional[str]
+ |
+
+
+
+ The column to use for splitting the lines. + |
+
+ None
+ |
+
multi_axis |
+
+ bool
+ |
+
+
+
+ Whether to use multiple y-axes. + |
+
+ False
+ |
+
smoothing |
+
+ Optional[DEXLineSmoothing]
+ |
+
+
+
+ The line smoothing to use:
+ - |
+
+ None
+ |
+
use_count |
+
+ bool
+ |
+
+
+
+ Whether to use the DEX_COUNT column for the y-axis. + |
+
+ False
+ |
+
bounding_type |
+
+ DEXBoundingType
+ |
+
+
+
+ The bounding type to use:
+ - |
+
+ 'absolute'
+ |
+
zero_baseline |
+
+ bool
+ |
+
+
+
+ Whether to use a zero base line. + |
+
+ False
+ |
+
combination_mode |
+
+ DEXCombinationMode
+ |
+
+
+
+ The combination mode to use ( |
+
+ 'avg'
+ |
+
return_view |
+
+ bool
+ |
+
+
+
+ Whether to return a |
+
+ False
+ |
+
**kwargs |
+ + | +
+
+
+ Additional keyword arguments to pass to the view metadata. + |
+
+ {}
+ |
+
src/dx/plotting/dex/basic_charts.py122 +123 +124 +125 +126 +127 +128 +129 +130 +131 +132 +133 +134 +135 +136 +137 +138 +139 +140 +141 +142 +143 +144 +145 +146 +147 +148 +149 +150 +151 +152 +153 +154 +155 +156 +157 +158 +159 +160 +161 +162 +163 +164 +165 +166 +167 +168 +169 +170 +171 +172 +173 +174 +175 +176 +177 +178 +179 +180 +181 +182 +183 +184 +185 +186 +187 +188 +189 +190 +191 +192 +193 +194 +195 +196 +197 +198 +199 +200 +201 +202 +203 +204 +205 +206 +207 +208 +209 +210 +211 +212 +213 | |
Coming soon!
+ + + + + +Coming soon!
+ + + + + + + + +Coming soon!
+ + + + + +Coming soon!
+ + + + + + + + +Coming soon!
+ + + + + +Coming soon!
+ + + + + + + + +
dx.pie(
+ df,
+ y='index',
+ split_slices_by='keyword_column',
+ show_total=False,
+ pie_label_type='annotation',
+ pie_label_contents='percent',
+)
+
+Make sure you enable dx as a pandas plotting backend first.
df.plot.pie(
+ y='index',
+ split_slices_by='keyword_column',
+ show_total=False,
+ pie_label_type='annotation',
+ pie_label_contents='percent',
+)
+
+src.dx.plotting.dex.basic_charts.pie(df, y, split_slices_by=None, show_total=True, pie_label_type='rim', pie_label_contents='name', return_view=False, **kwargs)
+
+Generates a DEX pie plot from the given DataFrame.
+ + + +Parameters:
+| Name | +Type | +Description | +Default | +
|---|---|---|---|
df |
+ + | +
+
+
+ The DataFrame to plot. + |
+ + required + | +
y |
+
+ str
+ |
+
+
+
+ The column to use for the slice size. + |
+ + required + | +
split_slices_by |
+
+ Optional[str]
+ |
+
+
+
+ The column to use for splitting the slices. If not provided, slices will be split and sized by |
+
+ None
+ |
+
show_total |
+
+ bool
+ |
+
+
+
+ Whether to show the total. + |
+
+ True
+ |
+
pie_label_type |
+
+ DEXPieLabelType
+ |
+
+
+
+ The pie label type to use:
+ - |
+
+ 'rim'
+ |
+
pie_label_contents |
+
+ DEXPieLabelContents
+ |
+
+
+
+ The pie label contents to use:
+ - |
+
+ 'name'
+ |
+
return_view |
+
+ bool
+ |
+
+
+
+ Whether to return a |
+
+ False
+ |
+
**kwargs |
+ + | +
+
+
+ Additional keyword arguments to pass to the view metadata. + |
+
+ {}
+ |
+
src/dx/plotting/dex/basic_charts.py220 +221 +222 +223 +224 +225 +226 +227 +228 +229 +230 +231 +232 +233 +234 +235 +236 +237 +238 +239 +240 +241 +242 +243 +244 +245 +246 +247 +248 +249 +250 +251 +252 +253 +254 +255 +256 +257 +258 +259 +260 +261 +262 +263 +264 +265 +266 +267 +268 +269 +270 +271 +272 +273 +274 +275 +276 +277 +278 +279 +280 | |
Coming soon!
+ + + + + +Coming soon!
+ + + + + + + + +Coming soon!
+ + + + + +Coming soon!
+ + + + + + + + +Coming soon!
+ + + + + +Coming soon!
+ + + + + + + + +Make sure you enable dx as a pandas plotting backend first.
Known Issue
+df.plot.scatter() can be used unless size is specified. If you wish to use pandas syntax and provide a size argument, please use df.plot(kind='scatter', ...).
+
dx.scatter(
+ df,
+ x='float_column',
+ y='integer_column',
+ size='index',
+ trend_line='polynomial',
+ marginal_graphics='histogram',
+ formula_display='r2'
+)
+
+Make sure you enable dx as a pandas plotting backend first.
Known Issue
+df.plot.scatter() can be used unless size is specified. If you wish to use pandas syntax and provide a size argument, please use df.plot(kind='scatter', ...).
df.plot(
+ kind='scatter',
+ x='float_column',
+ y='integer_column',
+ size='index',
+ trend_line='polynomial',
+ marginal_graphics='histogram',
+ formula_display='r2'
+)
+
+src.dx.plotting.dex.basic_charts.scatter(df, x, y, size=None, trend_line=None, marginal_graphics=None, formula_display=None, return_view=False, **kwargs)
+
+Generates a DEX scatterplot from the given DataFrame.
+ + + +Parameters:
+| Name | +Type | +Description | +Default | +
|---|---|---|---|
df |
+
+ DataFrame
+ |
+
+
+
+ The DataFrame to plot. + |
+ + required + | +
x |
+
+ str
+ |
+
+
+
+ The column to use for the x-axis. + |
+ + required + | +
y |
+
+ str
+ |
+
+
+
+ The column to use for the y-axis. + |
+ + required + | +
size |
+
+ Optional[str]
+ |
+
+
+
+ The column to use for sizing scatterplot points. + |
+
+ None
+ |
+
trend_line |
+
+ Optional[DEXTrendlineType]
+ |
+
+
+
+ The type of trendline to use. One of |
+
+ None
+ |
+
marginal_graphics |
+
+ Optional[DEXSummaryType]
+ |
+
+
+
+ The marginal graphics to use:
+ - |
+
+ None
+ |
+
formula_display |
+
+ Optional[DEXFormulaDisplay]
+ |
+
+
+
+ The formula display to use:
+ - |
+
+ None
+ |
+
return_view |
+
+ bool
+ |
+
+
+
+ Whether to return a |
+
+ False
+ |
+
**kwargs |
+ + | +
+
+
+ Additional keyword arguments to pass to the view metadata. + |
+
+ {}
+ |
+
src/dx/plotting/dex/basic_charts.py287 +288 +289 +290 +291 +292 +293 +294 +295 +296 +297 +298 +299 +300 +301 +302 +303 +304 +305 +306 +307 +308 +309 +310 +311 +312 +313 +314 +315 +316 +317 +318 +319 +320 +321 +322 +323 +324 +325 +326 +327 +328 +329 +330 +331 +332 +333 +334 +335 +336 +337 +338 +339 +340 +341 +342 +343 +344 +345 +346 +347 +348 +349 +350 +351 +352 +353 | |
Coming soon!
+ + + + + +Coming soon!
+ + + + + + + + +Coming soon!
+ + + + + +Coming soon!
+ + + + + + + + +Coming soon!
+ + + + + +Coming soon!
+ + + + + + + + +Coming soon!
+ + + + + +Coming soon!
+ + + + + + + + +Since dx.random_dataframe() returns integer_column values (-100 to 100) and float_column values (0.0 to 1.0) as the only numeric columns by default, we can suggest enabling the lat_float_column and lon_float_column arguments for some quick testing:
+
More about how Noteable builds with Mapbox here. 🗺ï¸
+Make sure you enable dx as a pandas plotting backend first.
df.plot.tilemap() directly
+
dx.tilemap(
+ df,
+ lat='lat_float_column',
+ lon='lon_float_column',
+ icon_opacity=0.5,
+ icon_size='index',
+ icon_size_scale="log",
+ stroke_color="magenta",
+ stroke_width=5,
+ label_column='bytes_column',
+ tile_layer="light",
+ hover_cols=['keyword_column', 'datetime_column'],
+)
+
+Make sure you enable dx as a pandas plotting backend first.
df.plot(
+ kind='tilemap',
+ lat='lat_float_column',
+ lon='lon_float_column',
+ icon_opacity=0.5,
+ icon_size='index',
+ icon_size_scale="log",
+ stroke_color="magenta",
+ stroke_width=5,
+ label_column='bytes_column',
+ tile_layer="light",
+ hover_cols=['keyword_column', 'datetime_column'],
+)
+df.plot.tilemap() directly
+
src.dx.plotting.dex.map_charts.tilemap(df, lat, lon, icon_opacity=1.0, icon_size=2, icon_size_scale='linear', stroke_color='#000000', stroke_width=2, label_column=None, tile_layer='streets', hover_cols=None, return_view=False, **kwargs)
+
+Generates a DEX tilemap from the given DataFrame.
+ + + +Parameters:
+| Name | +Type | +Description | +Default | +
|---|---|---|---|
df |
+ + | +
+
+
+ The DataFrame to plot. + |
+ + required + | +
lat |
+
+ str
+ |
+
+
+
+ The column to use for the latitude values. + |
+ + required + | +
lon |
+
+ str
+ |
+
+
+
+ The column to use for the longitude values. + |
+ + required + | +
icon_opacity |
+
+ float
+ |
+
+
+
+ The opacity to use for the icon ( |
+
+ 1.0
+ |
+
icon_size |
+
+ int
+ |
+
+
+
+ Either:
+- int: a fixed size to use for the icon ( |
+
+ 2
+ |
+
icon_size_scale |
+
+ DEXScale
+ |
+
+
+
+ The scale to use for functional sizing ( |
+
+ 'linear'
+ |
+
stroke_color |
+
+ Color
+ |
+
+
+
+ The color to use for the icon stroke. + |
+
+ '#000000'
+ |
+
stroke_width |
+
+ int
+ |
+
+
+
+ The width to use for the icon stroke. + |
+
+ 2
+ |
+
tile_layer |
+
+ str
+ |
+
+
+
+ The type of tile layer to use. One of |
+
+ 'streets'
+ |
+
return_view |
+
+ bool
+ |
+
+
+
+ Whether to return a |
+
+ False
+ |
+
**kwargs |
+ + | +
+
+
+ Additional keyword arguments to pass to the view metadata. + |
+
+ {}
+ |
+
src/dx/plotting/dex/map_charts.py26 + 27 + 28 + 29 + 30 + 31 + 32 + 33 + 34 + 35 + 36 + 37 + 38 + 39 + 40 + 41 + 42 + 43 + 44 + 45 + 46 + 47 + 48 + 49 + 50 + 51 + 52 + 53 + 54 + 55 + 56 + 57 + 58 + 59 + 60 + 61 + 62 + 63 + 64 + 65 + 66 + 67 + 68 + 69 + 70 + 71 + 72 + 73 + 74 + 75 + 76 + 77 + 78 + 79 + 80 + 81 + 82 + 83 + 84 + 85 + 86 + 87 + 88 + 89 + 90 + 91 + 92 + 93 + 94 + 95 + 96 + 97 + 98 + 99 +100 +101 +102 +103 +104 +105 +106 +107 +108 +109 +110 +111 +112 +113 +114 +115 +116 +117 +118 +119 +120 +121 +122 +123 +124 +125 +126 +127 +128 +129 +130 +131 +132 +133 +134 +135 +136 +137 +138 +139 +140 +141 +142 +143 +144 +145 +146 +147 +148 +149 +150 +151 +152 +153 | |
Coming soon!
+ + + + + +Coming soon!
+ + + + + + + + +Make sure you enable dx as a pandas plotting backend first.
df.plot.violin() directly
+
dx.violin(
+ df,
+ split_by='keyword_column',
+ metric='integer_column',
+ bins=5,
+ show_interquartile_range=True,
+ column_sort_order='desc',
+)
+
+Make sure you enable dx as a pandas plotting backend first.
df.plot(
+ kind='violin',
+ split_by='keyword_column',
+ metric='integer_column',
+ bins=5,
+ show_interquartile_range=True,
+ column_sort_order='desc',
+)
+df.plot.violin() directly
+
src.dx.plotting.dex.summary_charts.violin(df, split_by, metric, bins=30, show_interquartile_range=False, column_sort_order='asc', column_sort_type='string', return_view=False, **kwargs)
+
+Generates a DEX violin plot from the given DataFrame.
+ + + +Parameters:
+| Name | +Type | +Description | +Default | +
|---|---|---|---|
df |
+
+ DataFrame
+ |
+
+
+
+ The DataFrame to plot. + |
+ + required + | +
split_by |
+
+ str
+ |
+
+
+
+ The column to use for splitting the data. + |
+ + required + | +
metric |
+
+ str
+ |
+
+
+
+ The column to use to show distribution and density. + |
+ + required + | +
bins |
+
+ int
+ |
+
+
+
+ The number of bins to use for the violin plot. + |
+
+ 30
+ |
+
show_interquartile_range |
+
+ bool
+ |
+
+
+
+ Whether to show the interquartile range. + |
+
+ False
+ |
+
column_sort_order |
+
+ DEXSortColumnsByOrder
+ |
+
+
+
+ The order to sort the columns by. ( |
+
+ 'asc'
+ |
+
column_sort_type |
+
+ DEXSortColumnsByType
+ |
+
+
+
+ The type of sorting to use. ( |
+
+ 'string'
+ |
+
return_view |
+
+ bool
+ |
+
+
+
+ Whether to return a |
+
+ False
+ |
+
**kwargs |
+ + | +
+
+
+ Additional keyword arguments to pass to the view metadata. + |
+
+ {}
+ |
+
src/dx/plotting/dex/summary_charts.pyMake sure you enable dx as a pandas plotting backend first.
df.plot.wordcloud() directly
+
dx.wordcloud(
+ df,
+ word_column='dtype_column',
+ size='float_column',
+ text_format='token',
+ word_rotation='45',
+ random_coloring=True,
+)
+
+Make sure you enable dx as a pandas plotting backend first.
df.plot(
+ kind='wordcloud',
+ word_column='keyword_column',
+ word_column='dtype_column',
+ size='float_column',
+ text_format='token',
+ word_rotation='45',
+ random_coloring=True,
+)
+df.plot.wordcloud() directly
+
src.dx.plotting.dex.basic_charts.wordcloud(df, word_column, size, text_format='sentence', word_rotation=None, random_coloring=False, return_view=False, **kwargs)
+
+Generates a DEX wordcloud from the given DataFrame.
+ + + +Parameters:
+| Name | +Type | +Description | +Default | +
|---|---|---|---|
df |
+
+ DataFrame
+ |
+
+
+
+ The DataFrame to plot. + |
+ + required + | +
word_column |
+
+ str
+ |
+
+
+
+ The column to use for the words in the cloud. + |
+ + required + | +
size |
+
+ str
+ |
+
+
+
+ The column to use for the size of the words in the cloud. + |
+ + required + | +
text_format |
+
+ DEXTextDataFormat
+ |
+
+
+
+ The format of the text data. Either |
+
+ 'sentence'
+ |
+
word_rotation |
+
+ Optional[DEXWordRotate]
+ |
+
+
+
+ The rotation to use for the words in the cloud ( |
+
+ None
+ |
+
random_coloring |
+
+ bool
+ |
+
+
+
+ Whether to use random coloring for the words in the cloud. + |
+
+ False
+ |
+
return_view |
+
+ bool
+ |
+
+
+
+ Whether to return a |
+
+ False
+ |
+
**kwargs |
+ + | +
+
+
+ Additional keyword arguments to pass to the view metadata. + |
+
+ {}
+ |
+
src/dx/plotting/dex/basic_charts.py