cufflinks.datagen module¶
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cufflinks.datagen.bars(n=3, n_categories=3, prefix='category', columns=None, mode='abc')[source]¶ Returns a DataFrame with the required format for a bar plot
- n : int
- Number of points for each trace
- n_categories : int
- Number of categories for each point
- prefix : string
- Name for each category
- columns : [str]
- List of column names
- mode : string
- Format for each item
- ‘abc’ for alphabet columns ‘stocks’ for random stock names
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cufflinks.datagen.box(n_traces=5, n=100, mode=None)[source]¶ Returns a DataFrame with the required format for a box plot
- n_traces : int
- Number of traces
- n : int
- Number of points for each trace
- mode : string
- Format for each item
- ‘abc’ for alphabet columns ‘stocks’ for random stock names
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cufflinks.datagen.bubble(n_categories=5, n=10, prefix='category', mode=None)[source]¶ Returns a DataFrame with the required format for a bubble plot
- n_categories : int
- Number of categories
- n : int
- Number of points for each category
- prefix : string
- Name for each category
- mode : string
- Format for each item
- ‘abc’ for alphabet columns ‘stocks’ for random stock names
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cufflinks.datagen.bubble3d(n_categories=5, n=10, prefix='category', mode=None)[source]¶ Returns a DataFrame with the required format for a bubble3d plot
- n_categories : int
- Number of categories
- n : int
- Number of points for each trace
- prefix : string
- Name for each trace
- mode : string
- Format for each item
- ‘abc’ for alphabet columns ‘stocks’ for random stock names
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cufflinks.datagen.heatmap(n_x=5, n_y=10)[source]¶ Returns a DataFrame with the required format for a heatmap plot
- n_x : int
- Number of x categories
- n_y : int
- Number of y categories
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cufflinks.datagen.histogram(n_traces=1, n=500, mode=None)[source]¶ Returns a DataFrame with the required format for a box plot
- n_traces : int
- Number of traces
- n : int
- Number of points for each trace
- mode : string
- Format for each item
- ‘abc’ for alphabet columns ‘stocks’ for random stock names
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cufflinks.datagen.lines(n_traces=5, n=100, columns=None, dateIndex=True, mode=None)[source]¶ Returns a DataFrame with the required format for a scatter (lines) plot
- n_traces : int
- Number of traces
- n : int
- Number of points for each trace
- columns : [str]
- List of column names
- dateIndex : bool
- If True it will return a datetime index if False it will return a enumerated index
- mode : string
- Format for each item
- ‘abc’ for alphabet columns ‘stocks’ for random stock names
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cufflinks.datagen.ohlc(n=100)[source]¶ Returns a DataFrame with the required format for a candlestick or ohlc plot df[[‘open’,’high’,’low’,’close’]]
- n : int
- Number of ohlc points
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cufflinks.datagen.ohlcv(n=100)[source]¶ Returns a DataFrame with the required format for a candlestick or ohlc plot df[[‘open’,’high’,’low’,’close’,’volume’]
- n : int
- Number of ohlc points
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cufflinks.datagen.pie(n_labels=5, mode=None)[source]¶ Returns a DataFrame with the required format for a pie plot
- n_labels : int
- Number of labels
- mode : string
- Format for each item
- ‘abc’ for alphabet columns ‘stocks’ for random stock names
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cufflinks.datagen.scatter(n_categories=5, n=10, prefix='category', mode=None)[source]¶ Returns a DataFrame with the required format for a scatter plot
- n_categories : int
- Number of categories
- n : int
- Number of points for each category
- prefix : string
- Name for each category
- mode : string
- Format for each item
- ‘abc’ for alphabet columns ‘stocks’ for random stock names
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cufflinks.datagen.scatter3d(n_categories=5, n=10, prefix='category', mode=None)[source]¶ Returns a DataFrame with the required format for a scatter3d plot
- n_categories : int
- Number of categories
- n : int
- Number of points for each trace
- prefix : string
- Name for each trace
- mode : string
- Format for each item
- ‘abc’ for alphabet columns ‘stocks’ for random stock names