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Analyze CSV, JSON, Excel data; generate charts and reports. Use when the user provides data files or asks for statistics, charts, or data insights.
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| name | data-analysis |
| description | Analyze CSV, JSON, Excel data; generate charts and reports. Use when the user provides data files or asks for statistics, charts, or data insights. |
| metadata | {"ccbot":{"emoji":"📊","requires":{"bins":["uv"]}}} |
Use uv run with inline dependencies — no venv setup needed.
uv run --with pandas python3 - <<'EOF'
import pandas as pd, sys
df = pd.read_csv(sys.argv[1])
print(df.shape)
print(df.dtypes)
print(df.describe())
print("\nMissing values:")
print(df.isnull().sum())
EOF data.csv
# JSON
uv run --with pandas python3 -c "
import pandas as pd
df = pd.read_json('data.json')
print(df.head())
"
# Excel
uv run --with pandas,openpyxl python3 -c "
import pandas as pd
df = pd.read_excel('data.xlsx', sheet_name=0)
print(df.head())
"
mkdir -p output
uv run --with pandas,matplotlib python3 - <<'EOF'
import pandas as pd, matplotlib.pyplot as plt, sys
df = pd.read_csv(sys.argv[1])
# 折线图
fig, ax = plt.subplots(figsize=(10, 5))
df.set_index(df.columns[0])[df.columns[1]].plot(ax=ax, title=sys.argv[1])
plt.tight_layout()
plt.savefig("output/chart.png", dpi=150)
print("Saved: output/chart.png")
EOF data.csv
uv run --with pandas,matplotlib,seaborn python3 - <<'EOF'
import pandas as pd, matplotlib.pyplot as plt, seaborn as sns
df = pd.read_csv("data.csv")
fig, axes = plt.subplots(2, 2, figsize=(14, 10))
fig.suptitle("Data Report", fontsize=16)
# Distribution
df[df.select_dtypes("number").columns[0]].hist(ax=axes[0,0], bins=20)
axes[0,0].set_title("Distribution")
# Correlation heatmap
sns.heatmap(df.select_dtypes("number").corr(), ax=axes[0,1], annot=True, fmt=".2f")
axes[0,1].set_title("Correlation")
# Top categories
if df.select_dtypes("object").shape[1] > 0:
cat_col = df.select_dtypes("object").columns[0]
df[cat_col].value_counts().head(10).plot(kind="bar", ax=axes[1,0])
axes[1,0].set_title(f"Top {cat_col}")
plt.tight_layout()
plt.savefig("output/report.png", dpi=150, bbox_inches="tight")
print("Saved: output/report.png")
EOF
uv run --with pandas python3 - <<'EOF'
import pandas as pd
df = pd.read_csv("data.csv")
# 过滤
filtered = df[df["status"] == "active"]
# 分组聚合
summary = df.groupby("category").agg({"value": ["sum", "mean", "count"]})
# 输出
summary.to_csv("output/summary.csv")
print(summary.to_string())
EOF
df.head() first to understand the data shape.pd.read_csv(f, chunksize=10000).output/ for automatic Feishu delivery.tmux skill to run a Jupyter notebook.