• Do not write any import statements.

• Import statements will be ignored.

• These libraries are already available:

  • pandas as pd

  • numpy as np

  • json

  • pyecharts

• This code runs in a backend Python environment and must only produce ECharts JSON options (not render charts).

• The final output must be saved in a variable named result.

• The final output must be generated using:

  result = <chart>.dump_options()
  

  Example: result = bar.dump_options()

• Do not write files. Do not print. Do not return anything else.

• The pandas dataframe is always named df.

• Always work on a copy:

  df1 = df.copy()
  

Link events to chart elements by embedding an event_list inside each data item in the series values:

A) Required structure (when available)

• When event_uuid exists, each plotted element must be represented as a dict like:

  {"value": <number>, "event_list": [<uuid_str>, ...]}
  

B) Segment-level behavior

• For grouped/stacked/multi-series charts, each segment must carry only the events belonging to that exact segment.

C) Ordering + uniqueness

• Preserve original df order when collecting UUIDs.

• Do not include duplicates in event_list.

D) Fail-safe requirement

• If event_uuid does not exist, the code must still run and produce valid ECharts options JSON.

• In that case, set "event_list": [] for each data item (preferred) or omit event_list, but do not throw errors.

Use this as the baseline pattern. Users may change chart type/grouping/styling later, but must preserve output rules and event-linking structure.

# Step 1: Work on a copy
df1 = df.copy()

# Step 2: Count occurrences for each category
category_counts = df1["Primary category"].value_counts().sort_values(ascending=False)
categories = list(category_counts.index)

values = []
event_lists = []  # optional helper list; keep if useful

# Step 3: Collect event UUIDs and counts (fail-safe if event_uuid is missing)
for category in categories:
    category_rows = df1[df1["Primary category"] == category]

    event_uuids = []
    if "event_uuid" in df1.columns:
        for uuid in category_rows["event_uuid"].tolist():
            s = str(uuid)
            if s not in event_uuids:
                event_uuids.append(s)

    values.append({
        "value": int(category_counts[category]),
        "event_list": event_uuids
    })
    event_lists.append(event_uuids)

# Step 4: Create the bar chart
bar = (
    pyecharts.charts.Bar(
        init_opts=pyecharts.options.InitOpts(
            width="800px",
            height="600px",
            bg_color="black"
        )
    )
    .add_xaxis(categories)
    .add_yaxis(
        "Counts",
        values,
        itemstyle_opts=pyecharts.options.ItemStyleOpts(
            border_radius=[10, 10, 0, 0],
        ),
    )
    .set_global_opts(
        title_opts=pyecharts.options.TitleOpts(
            title="Overall player involvement in game",
            title_textstyle_opts=pyecharts.options.TextStyleOpts(color="white"),
        ),
        xaxis_opts=pyecharts.options.AxisOpts(
            name="Players",
            name_location="middle",
            name_gap="50",
            name_textstyle_opts=pyecharts.options.TextStyleOpts(color="white"),
            axislabel_opts=pyecharts.options.LabelOpts(rotate=45, color="white"),
            axisline_opts=pyecharts.options.AxisLineOpts(
                linestyle_opts=pyecharts.options.LineStyleOpts(color="rgba(128, 128, 128, 1)")
            ),
            splitline_opts=pyecharts.options.SplitLineOpts(is_show=False)
        ),
        yaxis_opts=pyecharts.options.AxisOpts(
            name="Number of actions",
            name_location="middle",
            name_gap="30",
            name_textstyle_opts=pyecharts.options.TextStyleOpts(color="white"),
            axislabel_opts=pyecharts.options.LabelOpts(color="white"),
            axisline_opts=pyecharts.options.AxisLineOpts(
                linestyle_opts=pyecharts.options.LineStyleOpts(color="rgba(128, 128, 128, 1)")
            ),
            splitline_opts=pyecharts.options.SplitLineOpts(
                linestyle_opts=pyecharts.options.LineStyleOpts(
                    color="rgba(128, 128, 128, 1)",
                    type_="dotted",
                    width=1
                ),
                is_show=True
            )
        ),
        tooltip_opts=pyecharts.options.TooltipOpts(trigger="item"),
        legend_opts=pyecharts.options.LegendOpts(is_show=False),
    )
    .set_series_opts(
        label_opts=pyecharts.options.LabelOpts(is_show=True, position="insideTop")
    )
)

# Step 5: Export chart options to JSON
result = bar.dump_options()

• Uses dataframe df

• Creates a copy df1 = df.copy()

• Embeds event linking into series values using dicts: {"value": ..., "event_list": [...]} when possible

• Preserves original df order in event_list and avoids duplicates

• Does not crash if event_uuid is missing

• Ends with result = <chart>.dump_options()


Users can modify only this section to change titles/labels/colors/layout. Example:

• Chart title must be: Invovlements of the players

• X-axis title must be: Players with their jersey numbers

• Y-axis title must be: Number of Involvements


• Ensure the X-axis title is not cut off. Increase bottom padding / grid bottom margin (e.g., set grid bottom to ~20–25%).


• Ensure the X-axis title is not cut off. Increase bottom padding / grid bottom margin (e.g., set grid bottom to ~20–25%).

• Use dark background

• Bars should be light yellow

• Axis title font size must be 14px