whatstk.graph

Plot tools using plotly.

Import plot (by plotly) to plot figures.

>>> from whatstk.graph import plot

whatstk.graph.base

Build plotly-compatible figures.

Classes

FigureBuilder([df, chat])

Generate a variety of figures from your loaded chat.

class whatstk.graph.base.FigureBuilder(df=None, chat=None)

Bases: object

Generate a variety of figures from your loaded chat.

Integrates feature extraction and visualization logic to automate data plots.

Note: Either df or chat must be provided.

Parameters:

• df (pandas.DataFrame, optional) – Chat data. Atribute df of a chat loaded using Chat. If a value is given, chat is ignored.
• chat (Chat, optional) – Chat data. Object obtained when chat loaded using Chat. Required if df is None.

Attributes

user_color_mapping	Get mapping between user and color.
usernames	Get list with users available in given chat.

Methods

user_interventions_count_linechart([…])	Plot number of user interventions over time.
user_message_responses_flow([title])	Get the flow of message responses.
user_message_responses_heatmap([norm, title])	Get the response matrix heatmap.
user_msg_length_boxplot([title, xlabel])	Generate figure with boxplots of each user’s message length.

property user_color_mapping

Get mapping between user and color.

Each user is assigned a color automatically, so that this color is preserved for that user in all to-be-generated plots.

Returns:dict – Mapping from username to color (rgb).

user_interventions_count_linechart(date_mode='date', msg_length=False, cumulative=False, all_users=False, title='User interventions count', xlabel='Date/Time', cummulative=None)

Plot number of user interventions over time.

Parameters:

• date_mode (str, optional) –

  Choose mode to group interventions by. Defaults to 'date'. Available modes are:

  • 'date': Grouped by particular date (year, month and day).
  • 'hour': Grouped by hours.
  • 'month': Grouped by months.
  • 'weekday': Grouped by weekday (i.e. monday, tuesday, …, sunday).
  • 'hourweekday': Grouped by weekday and hour.
• msg_length (bool, optional) – Set to True to count the number of characters instead of number of messages sent.
• cumulative (bool, optional) – Set to True to obtain commulative counts.
• all_users (bool, optional) – Obtain number of interventions of all users combined. Defaults to False.
• title (str, optional) – Title for plot. Defaults to “User interventions count”.
• xlabel (str, optional) – x-axis label title. Defaults to “Date/Time”.
• cummulative (bool, optional) – Deprecated, use cumulative.

Returns:

plotly.graph_objs.Figure – Plotly Figure.

See also

• get_interventions_count
• fig_scatter_time

Example

>>> from whatstk import WhatsAppChat
>>> from whatstk.graph import plot, FigureBuilder
>>> from whatstk.data import whatsapp_urls
>>> chat = WhatsAppChat.from_source(filepath=whatsapp_urls.LOREM)
>>> fig = FigureBuilder(chat=chat).user_interventions_count_linechart(cumulative=True)
>>> plot(fig)

user_message_responses_flow(title='Message flow')

Get the flow of message responses.

A response from user X to user Y happens if user X sends a message right after a message from user Y.

Uses a Sankey diagram.

Parameters:title (str, optional) – Title for plot. Defaults to “Message flow”. Returns:plotly.graph_objs.Figure – Plotly Figure.

See also

• get_response_matrix
• fig_sankey

Example

>>> from whatstk import WhatsAppChat
>>> from whatstk.graph import plot, FigureBuilder
>>> from whatstk.data import whatsapp_urls
>>> chat = WhatsAppChat.from_source(filepath=whatsapp_urls.LOREM)
>>> fig = FigureBuilder(chat=chat).user_message_responses_flow()
>>> plot(fig)

user_message_responses_heatmap(norm='absolute', title='Response matrix')

Get the response matrix heatmap.

A response from user X to user Y happens if user X sends a message right after a message from user Y.

Parameters:

• norm (str, optional) –

  Specifies the type of normalization used for reponse count. Can be:

  • 'absolute': Absolute count of messages.
  • 'joint': Normalized by total number of messages sent by all users.
  • 'sender': Normalized per sender by total number of messages sent by user.
  • 'receiver': Normalized per receiver by total number of messages sent by user.
• title (str, optional) – Title for plot. Defaults to “Response matrix”.

Returns:

plotly.graph_objs.Figure – Plotly Figure.

See also

• get_response_matrix
• fig_heatmap

Example

>>> from whatstk import WhatsAppChat
>>> from whatstk.graph import plot, FigureBuilder
>>> from whatstk.data import whatsapp_urls
>>> chat = WhatsAppChat.from_source(filepath=whatsapp_urls.LOREM)
>>> fig = FigureBuilder(chat=chat).user_message_responses_heatmap()
>>> plot(fig)

user_msg_length_boxplot(title='User message length', xlabel='User')

Generate figure with boxplots of each user’s message length.

Parameters:

• title (str, optional) – Title for plot. Defaults to “User message length”.
• xlabel (str, optional) – x-axis label title. Defaults to “User”.

Returns:

dict – Dictionary with data and layout. Plotly compatible.

See also

• fig_boxplot_msglen

Example

>>> from whatstk import WhatsAppChat
>>> from whatstk.graph import plot, FigureBuilder
>>> from whatstk.data import whatsapp_urls
>>> chat = WhatsAppChat.from_source(filepath=whatsapp_urls.LOREM)
>>> fig = FigureBuilder(chat=chat).user_msg_length_boxplot()
>>> plot(fig)

property usernames

Get list with users available in given chat.

Returns:list – List with usernames available in chat DataFrame.

whatstk.graph.figures

Build Plotly compatible Figures.

whatstk.graph.figures.boxplot

Boxplot figures.

Functions

fig_boxplot_msglen(df[, username_to_color, …])

Visualize boxplot.

whatstk.graph.figures.boxplot.fig_boxplot_msglen(df, username_to_color=None, title='', xlabel=None)

Visualize boxplot.

Parameters:

• df (pandas.DataFrame) – Chat data.
• username_to_color (dict, optional) –
• title (str, optional) – Title for plot. Defaults to “”.
• xlabel (str, optional) – x-axis label title. Defaults to None.

Returns:

plotly.graph_objs.Figure

whatstk.graph.figures.heatmap

Heatmap plot figures.

Functions

fig_heatmap(df_matrix[, title])

Generate heatmap figure from NxN matrix.

whatstk.graph.figures.heatmap.fig_heatmap(df_matrix, title='')

Generate heatmap figure from NxN matrix.

Parameters:

• df_matrix (pandas.DataFrame) – Matrix as DataFrame. Index values and column values must be equal.
• title (str) – Title of plot. Defaults to “”.

Returns:

plotly.graph_objs.Figure

whatstk.graph.figures.sankey

Sankey plot figures.

Functions

fig_sankey(label, color, source, target, value)

Generate sankey image.

whatstk.graph.figures.sankey.fig_sankey(label, color, source, target, value, title='')

Generate sankey image.

Parameters:

• label (list) – List with node labels.
• color (list) – List with node colors.
• source (list) – List with link source id.
• target (list) – List with linke target id.
• value (list) – List with link value.
• title (str, optional) – Title. Defaults to “”.

Returns:

plotly.graph_objs.Figure

whatstk.graph.figures.scatter

Scatter plot figures.

Functions

fig_scatter_time(user_data[, …])

Obtain Figure to plot using plotly.

whatstk.graph.figures.scatter.fig_scatter_time(user_data, username_to_color=None, title='', xlabel=None)

Obtain Figure to plot using plotly.

user_data must be a pandas.DataFrame with timestamps as index and a column for each user. You can easily generate suitable user_data using the function get_interventions_count (disclaimer: not compatible with date_mode='hourweekday').

Parameters:

• user_data (pandas.DataFrame) – Input data. Shape nrows x ncols, where nrows = number of timestaps and ncols = number of users.
• username_to_color (dict, optional) –
• title (str, optional) – Title of figure. Defaults to “”.
• xlabel (str, optional) – x-axis label title. Defaults to None.

Returns:

plotly.graph_objs.Figure

See also

• get_interventions_count

whatstk.graph.figures.utils

Utils for library plots.

Functions

hex_color_palette(n_colors)

Get palette of n_colors color hexadecimal codes.

whatstk.graph.figures.utils.hex_color_palette(n_colors)

Get palette of n_colors color hexadecimal codes.

Parameters:n_colors (int) – Size of the color palette.