Time Series Plots & Graphs

Story for Kids: Imagine drawing a line that connects the dots for your temperature every day. You can see how it goes up and down!

import matplotlib.pyplot as plt
plt.plot(data['date'], data['value'])
plt.xlabel('Date')
plt.ylabel('Value')
plt.title('Line Plot of Time Series')
plt.show()

Story for Kids: If you color each month's temperature with a different color, you can see which months are hottest or coldest every year!

import seaborn as sns
import pandas as pd
sns.lineplot(x='month', y='value', hue='year', data=data)
plt.title('Seasonal Plot')
plt.show()

Story for Kids: If you plot today's temperature against yesterday's, you can see if warm days follow warm days!

from pandas.plotting import lag_plot
lag_plot(data['value'])
plt.title('Lag Plot')
plt.show()

Story for Kids: It's like checking if your test scores are similar from one week to the next!

from statsmodels.graphics.tsaplots import plot_acf
plot_acf(data['value'])
plt.title('Autocorrelation (ACF) Plot')
plt.show()

Story for Kids: If you want to know if your score this week is related to two weeks ago, not just last week, use PACF!

from statsmodels.graphics.tsaplots import plot_pacf
plot_pacf(data['value'])
plt.title('Partial Autocorrelation (PACF) Plot')
plt.show()

Story for Kids: If you count how many days were hot, warm, or cold, you get a histogram!

plt.hist(data['value'], bins=20)
plt.xlabel('Value')
plt.ylabel('Frequency')
plt.title('Histogram')
plt.show()

Story for Kids: It's like a smooth version of a histogram, showing where your data is thickest!

import seaborn as sns
sns.kdeplot(data['value'])
plt.title('Density Plot')
plt.show()

Story for Kids: A boxplot is like a treasure chest that shows where most of your data is, and where the surprises are!

plt.boxplot(data['value'])
plt.title('Boxplot')
plt.show()

Story for Kids: A heatmap is like a colorful calendar that shows when things are hot or cold!

import seaborn as sns
sns.heatmap(data.pivot('month', 'year', 'value'))
plt.title('Heatmap')
plt.show()

Story for Kids: If you plot your height against your age, you get a scatter plot!

plt.scatter(data['x'], data['y'])
plt.xlabel('X')
plt.ylabel('Y')
plt.title('Scatter Plot')
plt.show()

Story for Kids: It's like taking apart a toy to see all the pieces—trend, seasonality, and noise!

from statsmodels.tsa.seasonal import seasonal_decompose
result = seasonal_decompose(data['value'], model='additive', period=12)
result.plot()
plt.show()

Story for Kids: If you draw your best guess for tomorrow's temperature, that's a forecast plot!

plt.plot(data['date'], data['value'], label='Actual')
plt.plot(data['date'], forecast, label='Forecast')
plt.fill_between(data['date'], lower, upper, color='gray', alpha=0.2, label='Interval')
plt.legend()
plt.title('Forecast Plot')
plt.show()

Story for Kids: If you check how far off your guesses were from the real answer, that's a residual plot!

plt.plot(data['date'], residuals)
plt.axhline(0, color='gray', linestyle='--')
plt.title('Residual Plot')
plt.show()

Story for Kids: If you look at your average score over the last 5 tests, and see how it changes, that's a rolling mean plot!

data['rolling_mean'] = data['value'].rolling(window=12).mean()
plt.plot(data['date'], data['value'], label='Original')
plt.plot(data['date'], data['rolling_mean'], label='Rolling Mean')
plt.legend()
plt.title('Rolling Mean Plot')
plt.show()

Story for Kids: If you want to know which "notes" (cycles) are loudest in your data, use a periodogram!

from scipy.signal import periodogram
freqs, psd = periodogram(data['value'])
plt.plot(freqs, psd)
plt.title('Periodogram / Spectral Plot')
plt.xlabel('Frequency')
plt.ylabel('Power')
plt.show()

Story for Kids: If you want to see if your test scores are like everyone's, you can line them up and compare!

import scipy.stats as stats
import matplotlib.pyplot as plt
stats.probplot(data['value'], dist="norm", plot=plt)
plt.title('QQ Plot')
plt.show()

Story for Kids: A violin plot looks like a squished balloon and shows where your data is thick or thin!

import seaborn as sns
sns.violinplot(y=data['value'])
plt.title('Violin Plot')
plt.show()

Story for Kids: If you want to see how all your test scores relate to each other, you can look at every pair!

import seaborn as sns
sns.pairplot(data)
plt.show()

Story for Kids: A calendar heatmap is like a colorful calendar that shows how busy or quiet each day was!

import calmap
import matplotlib.pyplot as plt
calmap.calendarplot(data['value'])
plt.title('Calendar Heatmap')
plt.show()

Story for Kids: If you want to see which of your friends are most alike, you can compare everyone to everyone!

import seaborn as sns
sns.heatmap(data.corr(), annot=True, cmap='coolwarm')
plt.title('Correlation Matrix Heatmap')
plt.show()

Story for Kids: Andrews curves are like drawing a squiggly line for each friend to see who is most similar!

from pandas.plotting import andrews_curves
andrews_curves(data, 'class_column')
plt.title('Andrews Curves')
plt.show()

Story for Kids: If you want to see how your scores in different subjects compare, you can draw a line for each subject!

from pandas.plotting import parallel_coordinates
parallel_coordinates(data, 'class_column')
plt.title('Parallel Coordinates Plot')
plt.show()

Story for Kids: If your allowance goes up in steps, a step plot shows each jump!

plt.step(data['date'], data['value'])
plt.title('Step Plot')
plt.show()

Story for Kids: If you color under your line plot, you get an area plot!

data['value'].plot.area()
plt.title('Area Plot')
plt.show()

Story for Kids: If you keep adding your scores together, you get a cumulative sum plot!

data['cumsum'] = data['value'].cumsum()
plt.plot(data['date'], data['cumsum'])
plt.title('Cumulative Sum Plot')
plt.show()

Story for Kids: A waterfall plot shows how each step adds up to the total, like building a tower block by block!

import waterfall_chart
waterfall_chart.plot(data['labels'], data['values'])
plt.title('Waterfall Plot')
plt.show()

Story for Kids: A polar plot is like drawing your data in a circle, like a clock!

plt.subplot(projection='polar')
plt.plot(theta, r)
plt.title('Polar Plot')
plt.show()

Story for Kids: A sunburst plot is like a colorful pie with many layers, showing how things are grouped!

import plotly.express as px
fig = px.sunburst(data, path=['group', 'subgroup'], values='value')
fig.show()

Story for Kids: A treemap is like a box of chocolates, where each piece shows how big or small something is!

import plotly.express as px
fig = px.treemap(data, path=['group', 'subgroup'], values='value')
fig.show()

Story for Kids: A dendrogram is like a family tree for your data, showing how things are related!

from scipy.cluster.hierarchy import dendrogram, linkage
Z = linkage(data, 'ward')
dendrogram(Z)
plt.title('Dendrogram')
plt.show()

Story for Kids: A fan chart is like a weather forecast that shows a wider and wider area for where the weather might be!

import matplotlib.pyplot as plt
plt.plot(dates, forecast, label='Forecast')
plt.fill_between(dates, lower, upper, color='blue', alpha=0.2, label='Uncertainty')
plt.title('Fan Chart')
plt.legend()
plt.show()

Story for Kids: Imagine drawing lots of lines for different friends' predictions on the same chart. It looks like spaghetti!

for forecast in forecasts:
    plt.plot(dates, forecast, alpha=0.3)
plt.title('Spaghetti Plot')
plt.show()

Story for Kids: Imagine your toy car suddenly changes direction. A change point plot shows where things change suddenly!

import ruptures as rpt
rpt.display(signal, bkps)
plt.title('Change Point Detection')
plt.show()

Story for Kids: This plot is like checking how far your guesses were from the real answer!

import matplotlib.pyplot as plt
plt.hist(errors, bins=30)
plt.title('Forecast Error Distribution')
plt.xlabel('Error')
plt.ylabel('Frequency')
plt.show()

Story for Kids: Imagine lots of little charts in a grid, each showing a different friend's story!

import matplotlib.pyplot as plt
fig, axes = plt.subplots(nrows, ncols, figsize=(12,8))
for i, ax in enumerate(axes.flat):
    ax.plot(data[i])
plt.tight_layout()
plt.show()

Story for Kids: It's like stacking colored sand in a bottle—each color shows a different part!

plt.stackplot(dates, series1, series2, series3)
plt.title('Stacked Area Plot')
plt.show()

Story for Kids: DTW is like matching two songs that go at different speeds, so they line up!

from dtaidistance import dtw
alignment = dtw.warping_path(series1, series2)
dtw.visualisation.plot_warping(series1, series2, alignment)
plt.show()

Story for Kids: Clustering is like grouping your toys by color or size!

from tslearn.clustering import TimeSeriesKMeans
model = TimeSeriesKMeans(n_clusters=3)
labels = model.fit_predict(data)
for i in range(3):
    plt.plot(data[labels==i].T, alpha=0.3)
plt.title('Time Series Clustering')
plt.show()

Story for Kids: This plot is like showing how much each friend helped you with your homework!

from statsmodels.tsa.ar_model import AutoReg
model = AutoReg(data, lags=4).fit()
plt.bar(range(1,5), model.params[1:])
plt.title('AR Coefficient Plot')
plt.show()

Story for Kids: This plot is like drawing arrows to show which friend started the game!

import networkx as nx
G = nx.DiGraph()
G.add_edge('A', 'B')
nx.draw(G, with_labels=True)
plt.title('Causality Network')
plt.show()