vamseeachanta
vamseeachanta/workspace-hub
相关职业SOC
基于 SOC 职业分类
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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.
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基于 SOC 职业分类
| name | autoviz-4-feature-analysis-and-distribution-plots |
| description | Sub-skill of autoviz: 4. Feature Analysis and Distribution Plots. |
| version | 1.0.0 |
| category | data-analysis |
| type | reference |
| scripts_exempt | true |
Understanding Feature Distributions:
from autoviz import AutoViz_Class
import pandas as pd
import numpy as np
# Create dataset with various distributions
np.random.seed(42)
df = pd.DataFrame({
# Normal distribution
"normal": np.random.normal(100, 15, 1000),
# Skewed distribution
"skewed": np.random.exponential(50, 1000),
# Bimodal distribution
"bimodal": np.concatenate([
np.random.normal(30, 5, 500),
np.random.normal(70, 5, 500)
]),
# Uniform distribution
"uniform": np.random.uniform(0, 100, 1000),
# Categorical with different frequencies
"category_balanced": np.random.choice(["A", "B", "C"], 1000),
"category_imbalanced": np.random.choice(
["Common", "Rare", "Very Rare"],
1000,
p=[0.8, 0.15, 0.05]
),
# Target variable
"target": np.random.choice([0, 1], 1000, p=[0.7, 0.3])
})
AV = AutoViz_Class()
# AutoViz will automatically:
# 1. Detect distribution types
# 2. Create appropriate histograms
# 3. Show box plots for numerical features
# 4. Create bar charts for categorical features
# 5. Highlight potential outliers
df_analyzed = AV.AutoViz(
filename="",
dfte=df,
depVar="target",
verbose=2,
chart_format="svg"
)
Categorical Feature Analysis:
from autoviz import AutoViz_Class
import pandas as pd
import numpy as np
# Dataset with multiple categorical features
df = pd.DataFrame({
"product_category": np.random.choice(
["Electronics", "Clothing", "Food", "Home", "Sports"],
1000
),
"customer_segment": np.random.choice(
["Premium", "Standard", "Budget"],
1000,
p=[0.2, 0.5, 0.3]
),
"region": np.random.choice(
["North", "South", "East", "West"],
1000
),
"channel": np.random.choice(
["Online", "Store", "Mobile"],
1000
),
"revenue": np.random.exponential(500, 1000),
"quantity": np.random.randint(1, 20, 1000)
})
AV = AutoViz_Class()
# AutoViz creates:
# - Bar charts for each categorical variable
# - Cross-tabulation visualizations
# - Category vs numerical variable plots
df_analyzed = AV.AutoViz(
filename="",
dfte=df,
depVar="revenue",
verbose=1,
chart_format=
)