mascarell / nikke_coredust

Core Dust by Outpost

Nikke Core Dust Breakpoints

While the exact core dust gain might vary between players, the chart generated by this Python notebook can provide valuable insights. By analyzing the core dust levels across different outpost levels, we can identify clear breakpoints. These breakpoints, represented by jumps in the bar chart, indicate significant increases in core dust production. Focusing on reaching these specific outpost levels can significantly improve your core dust acquisition rate in Nikke, ultimately benefiting character and equipment development.

The data is sourced from Reddit, Nikke Discord, & my personal outpost levels.

• Nikke Reddit: https://www.reddit.com/r/NikkeMobile/comments/1bh4n2v/about_core_dust/

Creating a Bar Chart in Python with Plotly

This code snippet utilizes the Plotly library in Python to create a bar chart visualizing the relationship between "outpost level" and "core dust".

1. Importing Library:

import plotly.graph_objects as go

• We import the plotly.graph_objects submodule as go. This submodule provides functionalities for creating various chart types in Plotly.

2. Data Preparation:

data = {
    "outpost level": [[117, 49], [126, 50], [135, 51], [145, 52], [155, 53], [165, 54], [176, 55], [187, 56], [199, 57], [210, 58], [223, 59], [235, 60], [248, 61], [262, 62], [276, 63], [286, 64], [293, 65], [301, 66], [308, 67], [316, 68], [323, 69], [331, 70], [338, 71], [346, 72], [354, 73], [362, 74], [370, 75], [378, 76], [386, 77], [395, 78]]
}

• A dictionary named data is created to store the data points. This data represents the "outpost level" (x-axis) and its corresponding "core dust" (y-axis) values.
• You can replace this data with your actual data points in a similar format (list of lists within a dictionary).

3. Chart Definition:

fig = go.Figure()

• We create a go.Figure object named fig. This object serves as the canvas for building the chart.

4. Adding Trace (Bars):

fig.add_trace(go.Bar(
    x=[row[0] for row in data["outpost level"]],  # Extract x-axis values (outpost level)
    y=[row[1] for row in data["outpost level"]],  # Extract y-axis values (core dust)
    name="Core Dust Level"
))

• The fig.add_trace method is used to add a chart element (trace) to the figure.
• Here, we add a go.Bar trace, which creates a bar chart.
  • x: This extracts the "outpost level" values (first element in each sub-list) from the data dictionary using list comprehension.
  • y: This extracts the "core dust" values (second element in each sub-list) from the data dictionary using list comprehension.
  • name: This sets the name of the bar series to be displayed in the legend ("Core Dust Level").

5. Customizing Layout:

fig.update_layout(
    title="Outpost Level vs. Core Dust",
    xaxis_title="Outpost Level",
    yaxis_title="Core Dust",
)

• The fig.update_layout method allows us to customize the overall layout of the chart.
  • title: This sets the main title of the chart ("Outpost Level vs. Core Dust").
  • xaxis_title: This sets the title for the x-axis ("Outpost Level").
  • yaxis_title: This sets the title for the y-axis ("Core Dust").

6. Saving as Webpage:

fig.write_html("outpost_core_dust.html")
print("Chart saved as outpost_core_dust.html")

• Finally, the fig.write_html method saves the created chart as an HTML file named "outpost_core_dust.html".
• The print statement confirms the successful saving of the chart.