DataFrame to Samples Dict
source link: https://datacrayon.com/posts/plotapi/data-wrangling/dataframe-to-samples-dict/
Go to the source link to view the article. You can view the picture content, updated content and better typesetting reading experience. If the link is broken, please click the button below to view the snapshot at that time.
Get the Books
Enjoying these notebooks and want to support the work? Check out the practical books on Data Science, Visualisation, and Evolutionary Algorithms.
Get the books
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.
Support this work
You can support this work by getting the e-books. This notebook will always be available for free in its online format.
Support this work
Get the practical book on data visualisation that shows you how to create static and interactive visualisations that are engaging and beautiful.
- Discounted Price that will grow as the book does,
- Code examples primarily in Python,
- Python Notebooks for each Section,
- Format: PDF download,
- Supplementary Video Tutorials,
- Unlimited access to updates.
Plotapi, beautiful by default.
Let plotapi do the heavy lifting – enabling beautiful interactive visualisations with a single line of code (instead of hundreds).
Get Plotapi
Recommend
-
7
OrderedDict vs dict in Python: The Right Tool for the Job
-
9
Dict Cc Dictionary – Bank NXTSkip to content Bank NXT If you want to copy vo...
-
6
超越 STL-map/hash_map性能的DICT 最近用纯C作了一个dict，根据试验，比 stl的 map快 2.3-2.5倍，比 hash_map快 1.3倍，主要用到的优化策略如下： 1. skiplist： 传统hash-bucket中，使用链表靠 next, 指针记录同一个 bucket中...
-
7
这是Redis源码阅读系列第一篇文章。 dict 是 redis 最重要的数据结构，db、hash、以及服务器内部需要用到hashmap的场景都是用dict来实现的。学习 dict 的源码，我们可以学到hashmap的原理及实现。 dict 数据结构哈希表元素节点...
-
7
一个奇怪的Python Dict表达式 从https://dbader.org/blog/python-mystery-dict-expression 中看到了一个很奇怪的dict表达式: {True: 'yes', 1: 'no', 1.0: 'm...
-
9
How to format a list dict as a table advertisements I have a dict containing lists of strings and was wanting...
-
13
-
7
Most languages will have builtin data structures such as array(list) or dict(hashtable). Is it interesting that whether bash has similar choices? Dict/Hashtable Create and...
-
5
In Python, a dictionary is a fat structure that is unordered by default. So, sometimes, you'll want to sort dictionaries by key or value to make queries easier. The problem is that sorting a dictionary by value is never a straightforward th...
-
7
December 22, 2022 /
About Joyk
Aggregate valuable and interesting links.
Joyk means Joy of geeK