DataFrame to Samples Dict
source link: https://datacrayon.com/posts/plotapi/data-wrangling/dataframe-to-samples-dict/
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Preamble¶
import pandas as pd
from plotapi import LineFight
LineFight.set_license("your username", "your license key")Introduction¶
Plotapi BarFight, PieFight, and LineFight, expect a list of dict items that define the value of nodes over time. The following is an example of this data structure.
samples = [
{"order": 2000.01, "name": "Sankey", "value": 10},
{"order": 2000.01, "name": "Terminus", "value": 10},
{"order": 2000.01, "name": "Chord", "value": 40},
{"order": 2000.01, "name": "Bar Fight", "value": 90},
{"order": 2000.01, "name": "Pie Fight", "value": 70},
{"order": 2000.02, "name": "Sankey", "value": 30},
{"order": 2000.02, "name": "Terminus", "value": 20},
{"order": 2000.02, "name": "Chord", "value": 40},
{"order": 2000.02, "name": "Bar Fight", "value": 120},
{"order": 2000.02, "name": "Pie Fight", "value": 55},
{"order": 2000.03, "name": "Sankey", "value": 35},
{"order": 2000.03, "name": "Terminus", "value": 45},
{"order": 2000.03, "name": "Chord", "value": 60},
{"order": 2000.03, "name": "Bar Fight", "value": 85},
{"order": 2000.03, "name": "Pie Fight", "value": 100},
{"order": 2000.04, "name": "Sankey", "value": 25},
{"order": 2000.04, "name": "Terminus", "value": 60},
{"order": 2000.04, "name": "Chord", "value": 90},
{"order": 2000.04, "name": "Bar Fight", "value": 50},
{"order": 2000.04, "name": "Pie Fight", "value": 105},
{"order": 2000.05, "name": "Sankey", "value": 60},
{"order": 2000.05, "name": "Terminus", "value": 80},
{"order": 2000.05, "name": "Chord", "value": 120},
{"order": 2000.05, "name": "Bar Fight", "value": 30},
{"order": 2000.05, "name": "Pie Fight", "value": 95},
]Dataset¶
Let's work backwards to the DataFrame, our starting point for this data wrangling exercise.
df = (
pd.DataFrame(samples)
.pivot(index="order", columns="name")["value"]
.reset_index()
.rename_axis(None, axis=1)
)
df| order | Bar Fight | Chord | Pie Fight | Sankey | Terminus | |
|---|---|---|---|---|---|---|
| 0 | 2000.01 | 90 | 40 | 70 | 10 | 10 |
| 1 | 2000.02 | 120 | 40 | 55 | 30 | 20 |
| 2 | 2000.03 | 85 | 60 | 100 | 35 | 45 |
| 3 | 2000.04 | 50 | 90 | 105 | 25 | 60 |
| 4 | 2000.05 | 30 | 120 | 95 | 60 | 80 |
Great! Now let's work back to the samples dict.
Wrangling¶
Our journey back to the samples list of dict items will be through pandas.melt.
df_melted = pd.melt(
df,
id_vars="order",
value_vars=list(df.columns[1:]),
var_name="name",
value_name="value",
)
df_melted.head(10)| order | name | value | |
|---|---|---|---|
| 0 | 2000.01 | Bar Fight | 90 |
| 1 | 2000.02 | Bar Fight | 120 |
| 2 | 2000.03 | Bar Fight | 85 |
| 3 | 2000.04 | Bar Fight | 50 |
| 4 | 2000.05 | Bar Fight | 30 |
| 5 | 2000.01 | Chord | 40 |
| 6 | 2000.02 | Chord | 40 |
| 7 | 2000.03 | Chord | 60 |
| 8 | 2000.04 | Chord | 90 |
| 9 | 2000.05 | Chord | 120 |
We're nearly there. This next step is optional - we're going to sort by order.
df_melted = df_melted.sort_values("order")
df_melted.head(10)| order | name | value | |
|---|---|---|---|
| 0 | 2000.01 | Bar Fight | 90 |
| 20 | 2000.01 | Terminus | 10 |
| 5 | 2000.01 | Chord | 40 |
| 15 | 2000.01 | Sankey | 10 |
| 10 | 2000.01 | Pie Fight | 70 |
| 1 | 2000.02 | Bar Fight | 120 |
| 21 | 2000.02 | Terminus | 20 |
| 6 | 2000.02 | Chord | 40 |
| 16 | 2000.02 | Sankey | 30 |
| 11 | 2000.02 | Pie Fight | 55 |
Now for the final step - let's get our list of dict items.
samples = df_melted.to_dict(orient="records")
samples[{'order': 2000.01, 'name': 'Bar Fight', 'value': 90},
{'order': 2000.01, 'name': 'Terminus', 'value': 10},
{'order': 2000.01, 'name': 'Chord', 'value': 40},
{'order': 2000.01, 'name': 'Sankey', 'value': 10},
{'order': 2000.01, 'name': 'Pie Fight', 'value': 70},
{'order': 2000.02, 'name': 'Bar Fight', 'value': 120},
{'order': 2000.02, 'name': 'Terminus', 'value': 20},
{'order': 2000.02, 'name': 'Chord', 'value': 40},
{'order': 2000.02, 'name': 'Sankey', 'value': 30},
{'order': 2000.02, 'name': 'Pie Fight', 'value': 55},
{'order': 2000.03, 'name': 'Terminus', 'value': 45},
{'order': 2000.03, 'name': 'Sankey', 'value': 35},
{'order': 2000.03, 'name': 'Pie Fight', 'value': 100},
{'order': 2000.03, 'name': 'Chord', 'value': 60},
{'order': 2000.03, 'name': 'Bar Fight', 'value': 85},
{'order': 2000.04, 'name': 'Pie Fight', 'value': 105},
{'order': 2000.04, 'name': 'Chord', 'value': 90},
{'order': 2000.04, 'name': 'Sankey', 'value': 25},
{'order': 2000.04, 'name': 'Bar Fight', 'value': 50},
{'order': 2000.04, 'name': 'Terminus', 'value': 60},
{'order': 2000.05, 'name': 'Pie Fight', 'value': 95},
{'order': 2000.05, 'name': 'Chord', 'value': 120},
{'order': 2000.05, 'name': 'Sankey', 'value': 60},
{'order': 2000.05, 'name': 'Bar Fight', 'value': 30},
{'order': 2000.05, 'name': 'Terminus', 'value': 80}]
Perfect! We're all done.
Visualisation¶
No Plotapi exercise is complete without a visualisation.
As we can see, we have set our license details in the preamble with LineFight.set_license().
Here we're using .show() which outputs to a Jupyter Notebook cell, however, we may want to output to an HTML file with .to_html() instead.
LineFight(samples, format_current_order="0.2f").show()Plotapi - Line Fight Diagram
Here we can see the default behaviour of Plotapi LineFight.
You can do so much more than what's presented in this example, and we'll cover this in later sections. If you want to see the full list of growing features, check out the Plotapi Documentation.
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