Visualization tool that makes it easier to get scatter plots right.
Project description
quantile_scatter
下の方に日本語の説明があります
Overview
- Visualization tool that makes it easier to get scatter plots right.
- The number of uniform data is divided into intervals on the x-axis, and the quantile points for each interval are displayed.
Usage
import quantile_scatter
# dummy data
x_ls = [(4 * random.random() - 2) ** 3
for _ in range(1000)]
y_ls = [math.sin(x) + random.random() * 0.5
for x in x_ls]
# plot [quantile_scatter]
quantile_scatter.plot(
x = x_ls, # x-list
y = y_ls, # y-list
min_bin_ratio = 1/20, # Ratio of the smallest group (the number of records in the smallest group as a percentage of the total)
ile_ls = [0.25, 0.5, 0.75]
)
Advanced Usage
- Option argument of
quantile_scatter.plot()
function:
mean = True # Also draw the "mean"
show = False # Do not show the graph and only return the data to be displayed (useful for saving the graph or drawing with something other than matplotlib)
missing_values = [None] # The specification that x contains a missing value of "None". Internally, the value is included in the statistics as "missing".
概要
- 散布図を正しく把握しやすくする可視化ツール
- 均一データ数の横軸区間に分け、各区間の分位点を表示する
- 説明は執筆中です
使用例
import quantile_scatter
# ダミーデータ
x_ls = [(4 * random.random() - 2) ** 3
for _ in range(1000)]
y_ls = [math.sin(x) + random.random() * 0.5
for x in x_ls]
# 分位点散布図の描画 [quantile_scatter]
quantile_scatter.plot(
x = x_ls, # 横軸数値リスト
y = y_ls, # 縦軸数値リスト
min_bin_ratio = 1/20, # 最小グループ割合 (最も小さいグループのレコード数が全体に占める割合)
ile_ls = [0.25, 0.5, 0.75] # どこの分位点を出すか
)
発展的な利用方法
quantile_scatter.plot()
関数のoption引数
mean = True # 「平均」も描画する
show = False # グラフ表示せず、表示対象データのみを返却 (グラフを保存したい場合や、matplotlib以外で描画したい場合などに有効)
missing_values = [None] # xにNoneという欠損値が含まれるという指定。内部的には"missing"という値として集計に含められる。
Project details
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