单变量分析
独立分析每一列：为数值列绘制分布图，为分类列统计值计数，并留意异常值和偏度。
单变量分析 是 CoddyKit 上的免费 Pandas & NumPy Academy 课时。 这是第 2 节课，共 4 节。 你可以在下方免费阅读本课时的完整内容 — 然后在浏览器中使用内置代码编辑器和全天候 AI 导师进行实践。 这是 Pandas & NumPy Academy 学习路径的一部分，你的进度在网页和 CoddyKit 应用中同步。 Pandas & NumPy Academy 课程共包含 4 节课。
本课时的部分内容尚未翻译，以英文显示。
What Is Univariate Analysis?
Univariate analysis examines one column at a time in isolation, ignoring relationships between columns. The goal is to understand each variable's distribution, detect outliers, identify skewness, and spot data quality issues before any modelling begins. Univariate analysis differs for numeric and categorical columns — numerics need distribution plots and summary statistics, while categoricals need frequency counts and proportions.
import pandas as pd
import seaborn as sns
df = sns.load_dataset('titanic')
numeric_cols = df.select_dtypes('number').columns.tolist()
categoric_cols = df.select_dtypes('object').columns.tolist()
print('Numeric columns:', numeric_cols)
print('Categorical columns:', categoric_cols)Histograms for Numeric Columns
Plot a histogram for each numeric column to see the shape of its distribution. Look for symmetry vs. skewness (tail on one side), modality (one peak vs. several), and obvious anomalies (values at extreme ends that seem implausible). In Pandas, df.hist() creates a quick multi-column histogram grid without writing a loop, but Seaborn's histplot gives finer control for individual columns.
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
df = sns.load_dataset('titanic')
# Quick multi-column histogram grid
numeric = df.select_dtypes('number')
numeric.hist(bins=20, figsize=(12, 8), layout=(2, 3), color='steelblue', edgecolor='white')
plt.suptitle('Univariate Distributions (Numeric Columns)', y=1.02)
plt.tight_layout()
plt.show()Summary Statistics for Numeric Columns
For each numeric column, compute and compare mean vs. median. When mean > median significantly, the distribution is right-skewed (long right tail, common for income and price data). When mean < median, it is left-skewed. Also check standard deviation relative to the mean: a std larger than the mean for a non-negative variable is a sign of high variability or outliers. Compute skewness directly with df.skew().
import pandas as pd
import seaborn as sns
df = sns.load_dataset('titanic')
numeric = df.select_dtypes('number')
summary = pd.DataFrame({
'mean': numeric.mean(),
'median': numeric.median(),
'std': numeric.std(),
'skewness': numeric.skew(),
'missing_pct': (numeric.isna().sum() / len(df) * 100).round(1)
}).round(2)
print(summary)Box Plots for Outlier Detection
Box plots are the quickest visual tool for spotting outliers in numeric columns. Any point beyond the whiskers (1.5×IQR from Q1 or Q3) is plotted individually and flagged as a potential outlier. A column with many outlier points warrants investigation: are they data entry errors, legitimate extreme values, or evidence of a non-normal distribution? Box plots also reveal right or left skew from the asymmetric position of the median inside the box.
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
df = sns.load_dataset('titanic')
fig, axes = plt.subplots(1, 3, figsize=(14, 5))
for ax, col in zip(axes, ['age', 'fare', 'sibsp']):
df[[col]].boxplot(ax=ax)
ax.set_title(f'Box Plot: {col}')
plt.tight_layout()
plt.show()IQR-Based Outlier Counts
The IQR (Interquartile Range) method defines outliers as values below Q1 - 1.5×IQR or above Q3 + 1.5×IQR. You can compute this programmatically to count and inspect outliers in every numeric column without plotting. This is useful in automated pipelines where you need to log anomaly counts rather than generate charts. Deciding what to do with outliers — remove, cap, or flag — should be a deliberate choice informed by domain knowledge.
import pandas as pd
import seaborn as sns
df = sns.load_dataset('titanic')
numeric = df.select_dtypes('number')
Q1 = numeric.quantile(0.25)
Q3 = numeric.quantile(0.75)
IQR = Q3 - Q1
lower = Q1 - 1.5 * IQR
upper = Q3 + 1.5 * IQR
outlier_counts = ((numeric < lower) | (numeric > upper)).sum()
print('Outlier counts per column:')
print(outlier_counts[outlier_counts > 0])Value Counts for Categorical Columns
For each categorical column, call value_counts() to see how observations are distributed across categories. Always check: does any single category dominate (>80%)? This causes class imbalance in classification tasks. Are there rare categories with only 1-2 observations (typos or data entry errors)? Does the distribution match business expectations (e.g. a 'country' column that shows most orders from an unexpected country)?
import pandas as pd
import seaborn as sns
df = sns.load_dataset('titanic')
for col in ['sex', 'embarked', 'class', 'who']:
counts = df[col].value_counts(dropna=False)
pct = (counts / len(df) * 100).round(1)
result = pd.DataFrame({'count': counts, 'pct%': pct})
print(f'\n--- {col} ---')
print(result)Bar Charts for Categorical Variables
Visualise categorical value counts as bar charts using sns.countplot or by calling value_counts().plot(kind='bar'). Sort bars from most to least frequent so the viewer can immediately see dominant categories. For binary variables (yes/no, male/female), a pie chart is acceptable — but for more than 3 categories, bar charts are always clearer. Rotate tick labels 45 degrees when category names are long.
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
df = sns.load_dataset('titanic')
fig, axes = plt.subplots(1, 2, figsize=(12, 5))
sns.countplot(data=df, x='embarked', order=df['embarked'].value_counts().index, ax=axes[0])
axes[0].set_title('Embarkation Port Counts')
sns.countplot(data=df, x='class', order=['First', 'Second', 'Third'], ax=axes[1])
axes[1].set_title('Passenger Class Counts')
plt.tight_layout()
plt.show()Detecting Skewness and Applying Transforms
Highly right-skewed numeric columns (skewness > 1.5) often benefit from a log transform to make them more symmetric. This is important for many statistical tests and some ML algorithms that assume normality. Apply np.log1p() (log(x+1), safe for values near zero) and re-check skewness. Compare histograms before and after to confirm the distribution became more bell-shaped.
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
df = sns.load_dataset('titanic')
fig, axes = plt.subplots(1, 2, figsize=(10, 4))
sns.histplot(df['fare'], bins=30, kde=True, ax=axes[0])
axes[0].set_title(f'fare (skew={df["fare"].skew():.2f})')
log_fare = np.log1p(df['fare'])
sns.histplot(log_fare, bins=30, kde=True, ax=axes[1], color='coral')
axes[1].set_title(f'log1p(fare) (skew={log_fare.skew():.2f})')
plt.tight_layout()
plt.show()Checking for Zero Variance Columns
A column with zero variance (all values are identical) provides no information to any model and should be dropped. A column with near-zero variance (99% of rows have the same value) is similarly useless. Compute variance with df.var() and filter. Also check for columns where nunique() == len(df) — these are likely ID columns that should not be used as features but may be useful as row identifiers.
import pandas as pd
import seaborn as sns
df = sns.load_dataset('titanic')
numeric = df.select_dtypes('number')
# Zero-variance columns
zero_var = numeric.columns[numeric.var() == 0].tolist()
print('Zero-variance columns:', zero_var)
# Near-zero variance (std < 0.01)
nzv = numeric.columns[numeric.std() < 0.01].tolist()
print('Near-zero variance:', nzv)
# Likely ID columns (all unique values)
id_candidates = [c for c in df.columns if df[c].nunique() == len(df)]
print('Likely ID columns:', id_candidates)Univariate Analysis Automation Loop
Rather than repeating the same analysis manually for each column, write a loop that iterates over all numeric columns and prints key statistics in a structured format. This makes it easy to scan dozens of columns quickly and flag those that need attention. The output can be saved to a CSV file as part of a pipeline audit log that stakeholders can review before data is used in modelling.
import pandas as pd
import seaborn as sns
df = sns.load_dataset('titanic')
numeric = df.select_dtypes('number')
rows = []
for col in numeric.columns:
s = df[col]
Q1, Q3 = s.quantile(0.25), s.quantile(0.75)
IQR = Q3 - Q1
n_outliers = ((s < Q1 - 1.5*IQR) | (s > Q3 + 1.5*IQR)).sum()
rows.append({
'column': col,
'missing_pct': round(s.isna().mean() * 100, 1),
'mean': round(s.mean(), 2),
'std': round(s.std(), 2),
'skew': round(s.skew(), 2),
'outliers': int(n_outliers)
})
report = pd.DataFrame(rows)
print(report.to_string(index=False))Documenting Univariate Findings
After completing univariate analysis, document your findings systematically. For each column note: the distribution shape (skewed, bimodal, normal-ish), missing value percentage and planned imputation strategy, outlier count and decision (cap, remove, flag), and any suspicious values requiring domain clarification. This documentation becomes the data quality log that guides the cleaning steps and protects decisions from being forgotten or contested later.
Quick Check
Test your understanding of univariate analysis from this lesson.
Lesson Recap
In this lesson you learned: histograms reveal distribution shape for numeric columns, box plots and IQR fencing identify outliers, and value_counts reveals category frequency for categorical columns. Automating these checks in a loop creates a reusable quality report. Next up we explore bivariate and correlation analysis — understanding relationships between pairs of columns.
常见问题解答
「单变量分析」课时是免费的吗？
是的 — 「单变量分析」的完整文本可在网页上免费阅读。要进行交互式练习（内置代码编辑器和全天候 AI 导师）并解锁 Pandas & NumPy Academy 课程的其余内容，请升级到 CoddyKit PRO。 Pandas & NumPy Academy 课程共包含 4 节课。
「单变量分析」这节课中我会学到什么？
独立分析每一列：为数值列绘制分布图，为分类列统计值计数，并留意异常值和偏度。 你通过在浏览器中直接运行的动手代码来练习 Pandas & NumPy Academy，全天候 AI 导师会在你学习这节课的过程中回答你的问题。
学习 Pandas & NumPy Academy 需要有经验吗？
无需任何先前经验。CoddyKit 上的 Pandas & NumPy Academy 课程适合初学者到高级学习者，你可以从这里开始或从头开始，按照自己的节奏学习。 这是第 2 节课，共 4 节。
「单变量分析」课时需要多长时间？
大多数 CoddyKit 课程大约需要 5–10 分钟。每节课都很精短且互动，所以你能稳步进步，并在网页和应用中从离开的地方继续。
我能在这节 Pandas & NumPy Academy 课中编写并运行代码吗？
能。每节 Pandas & NumPy Academy 课都包含内置代码编辑器，你可以在浏览器中直接编写并运行真实代码，并获得即时 AI 反馈 — 无需本地设置。