A First Scatter Plot
- One dot per patient: age on x, height on y
python
import matplotlib.pyplot as plt
plt.scatter(age, height)
plt.show()
Worked Example: Which Ticks?
python
plt.xticks([i * 5 for i in range(12)],
fontsize=15)text
[0, 5, 10, 15, 20, 25, 30, 35, 40, 45, 50, 55]range(12) gives i = 0 to 11, times 5: twelve ticks up to 55.
Bar Chart
One number across named items; every bar starts at zero
python
plt.figure(figsize=(12, 4))
plt.bar(x=people, height=weight,
width=0.6, color='orange',
edgecolor='k', alpha=0.6)
plt.xticks(fontsize=14, rotation=30)
plt.show()width=0.6: each bar fills 60% of its slotalpha=0.6: partly transparentrotation=30: tilted labels
Step 3 of 3
Step 3: Check Against the Plot
- Counts
1, 1, 2, 4, 4add up to 12, one per patient plt.histreturns the same counts and edges- The two short bars on the left are the two children
A Month of Polls, Drawn as Lines
python
np.random.seed(0) # added
days = np.arange(1, 31)
candidate_A = 50 + days * 0.07 \
+ 2 * np.random.randn(30)
candidate_B = 50 - days * 0.1 \
+ 3 * np.random.randn(30)
plt.style.use('fivethirtyeight')
plt.plot(days, candidate_A, 'o-', c='blue')
plt.plot(days, candidate_B, '^-', c='green')- A: +0.07 a day, B: -0.1 a day, plus random noise
plt.style.usestays on for every later plot
Step 5 of 5
Step 5: The Mean, and the Check
python
plt.boxplot(x=[height], showmeans=True)μ = 189412 ≈ 157.83
The Notebook's Boxplot of the Polls
python
plt.style.use('ggplot')
plt.boxplot(x=[candidate_A, candidate_B],
showmeans=True)
plt.xticks([1, 2], ['Candidate A', 'Candidate B'])| A | B | |
|---|---|---|
| Median | 51.947 | 47.389 |
| Q1 to Q3 | 50.87 to 53.53 | 45.35 to 48.89 |
| Outlier | 46.364 | 54.452 |
| Mean | 51.971 | 47.581 |
Pandas Plots Straight from a DataFrame
python
df.plot.scatter('Alcohol', 'Color intensity')
plt.show()- Name the columns; no lists to pass
- Pandas calls Matplotlib for you, so
plt.xlabeland friends still work - Each dot is one wine
Worked Example: 20 Bins of Alcohol
python
df['Alcohol'].plot.hist(bins=20, figsize=(5, 5),
edgecolor='k')w = 14.83 − 11.0320 = 3.8020 = 0.19
Seaborn: One Box per Class
python
import seaborn as sns
sns.boxplot(x='Class', y='Alcohol', data=df)
Violin Plot
python
sns.violinplot(x='Class', y='Alcohol', data=df)- Violin plot
- A boxplot plus the shape of the distribution: the width at each height shows how many wines have that value.
regplot: A Scatter Plot with a Fitted Line
python
sns.regplot(x='Alcohol', y='Color intensity',
data=df)- The line that fits the dots best: a linear regression, next week's topic
- The shaded band is the confidence interval: how unsure the line is
- Fitted here: slope 1.560, intercept -15.226
lmplot: One Line per Group
python
sns.lmplot(x='Alcohol', y='Color intensity',
hue='Class', data=df)hue='Class'colours the dots by class and fits one line per class
| Class | Slope |
|---|---|
| 1 | 1.094 |
| 2 | 0.464 |
| 3 | 1.527 |
The Correlation Matrix and Its Heatmap
python
corr_mat = np.corrcoef(df, rowvar=False)
corr_mat.shape # (14, 14)
corr_df = pd.DataFrame(corr_mat,
columns=df.columns, index=df.columns)
sns.heatmap(corr_df, linewidth=1,
cmap='plasma')rowvar=False: the variables are the columns- 14 x 14 = 196 values of r
- Diagonal is 1; the matrix is symmetric
- Yellow: r near 1; dark blue: most negative
