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Sub-skill of autoviz: 1. Basic One-Line EDA.
デフォルトでは、最初にソースを確認する Prompt が選択されています。直接コマンドに切り替えるか、ローカルコピーをダウンロードすることもできます。
インストールを決める前に、SKILL.md と SkillsMP に表示されている付属ファイルをお読みください。
SOC 職業分類に基づく
Codex または Claude でインストール この Prompt をコピーして Codex、Claude、または他のアシスタントに貼り付けると、Skill ページを確認してインストールできます。
直接コマンドでは確認用 Prompt が省略されます。実行前にソースを確認してください。
npx skills add https://github.com/vamseeachanta/workspace-hub --skill autoviz-1-basic-one-line-edaコマンドは1行のまま表示されます。コピー前に横へスクロールして全体を確認してください。
ローカルで確認しますか？SkillsMP が現在取得できるファイルをダウンロードできます。
SKILL.md を表示中
Write outbound email and external messages in Vamsee Achanta's voice — a subtle offer to help, never bold or rash claims. Load before drafting ANY email, LinkedIn/Collide reply, proposal note, or outreach sent under his name.
Save/publish analysis or computation results from ANY ecosystem repo to Hugging Face as a queryable, viewer-renderable dataset. Use when the user wants to "save results to hugging face", "publish dataset to HF", "hugging face data saving", "save analysis results", "hf dataset", "make results queryable", or "render via datasets-server API". Reshapes nested results into flat parquet tables, writes a dataset card with a viewer `configs:` block and provenance, applies license/public-vs-private routing, enforces a domain data-quality gate (faithful-to-source != correct), publishes to `aceengineer/<repo>-<projection>`, and verifies via the datasets-server API.
Clone, create, fork, configure, and manage GitHub repositories. Manage remotes, secrets, releases, and workflows. Works with gh CLI or falls back to git + GitHub REST API via curl.
| name | autoviz-1-basic-one-line-eda |
| description | Sub-skill of autoviz: 1. Basic One-Line EDA. |
| version | 1.0.0 |
| category | data-analysis |
| type | reference |
| scripts_exempt | true |
Simplest Usage:
from autoviz import AutoViz_Class
# Initialize AutoViz
AV = AutoViz_Class()
# Automatic visualization with one line
# Returns a dataframe and generates all charts
df_analyzed = AV.AutoViz(
filename="data.csv",
sep=",",
depVar="", # Target variable (optional)
dfte=None, # Pass DataFrame directly instead of filename
header=0,
verbose=1, # 0=minimal, 1=medium, 2=detailed output
lowess=False,
chart_format="svg",
max_rows_analyzed=150000,
max_cols_analyzed=30
)
print(f"Analyzed {df_analyzed.shape[0]} rows, {df_analyzed.shape[1]} columns")
From DataFrame:
from autoviz import AutoViz_Class
import pandas as pd
# Load your data
df = pd.read_csv("sales_data.csv")
# Or create sample data
df = pd.DataFrame({
"revenue": [100, 200, 150, 300, 250, 400, 350, 500],
"units": [10, 20, 15, 30, 25, 40, 35, 50],
"category": ["A", "B", "A", "B", "A", "B", "A", "B"],
"region": ["North", "South", "East", "West", "North", "South", "East", "West"],
"profit": [20, 40, 30, 60, 50, 80, 70, 100],
"customer_age": [25, 35, 45, 55, , , , ]
})
AV = AutoViz_Class()
df_result = AV.AutoViz(
filename=,
sep=,
depVar=,
dfte=df,
header=,
verbose=,
chart_format=
)
With Target Variable Analysis:
from autoviz import AutoViz_Class
import pandas as pd
# Classification dataset
df_classification = pd.DataFrame({
"feature_1": [1.2, 2.3, 1.5, 3.4, 2.1, 4.5, 3.2, 5.1],
"feature_2": [0.5, 1.2, 0.8, 2.1, 1.0, 3.2, 2.4, 4.0],
"feature_3": ["low", "medium", "low", "high", "medium", "high", "medium", "high"],
"target": [0, 0, 0, 1, 0, 1, 1, 1]
})
AV = AutoViz_Class()
# Specify target variable for focused analysis
df_analyzed = AV.AutoViz(
filename="",
sep=",",
depVar="target", # Target variable for classification
dfte=df_classification,
header=0,
verbose=2, # More detailed output
chart_format="svg"
)
# Regression dataset
df_regression = pd.DataFrame({
"size": [, , , , , , , ],
: [, , , , , , , ],
: [, , , , , , , ],
: [, , , , , , , ],
: [, , , , , , , ]
})
df_analyzed = AV.AutoViz(
filename=,
sep=,
depVar=,
dfte=df_regression,
header=,
verbose=,
chart_format=
)