Matplotlib for Climate Scientists & Meteorologists¶
Agenda - Learn the basics of the Matplotlib library. - Understand how to create various types of plots (line, bar, histogram, scatter, heatmap). - Customize plots with titles, labels, legends, and styles. - Save plots as image files (PNG, PDF, SVG, EPS, and PGF).
Introduction to Matplotlib - Matplotlib is a Python 2D/3D plotting library which produces scientific and publication-quality figures.
# Set working directory
import os
os.chdir("c:\\Users\\yonas\\Documents\\ICPAC\\python-climate")
processed_data_dir = os.path.join("data", "processed")
raw_data_dir = os.path.join("data", "raw")
Synthetic Climate Dataset¶
rng = np.random.default_rng(7)
stations = [("ADD","Addis Ababa",9.03,38.74),
("NBO","Nairobi",-1.29,36.82),
("MOG","Mogadishu",2.05,45.32)]
dates = pd.date_range("2020-01-01","2020-12-31",freq="D")
doy = dates.dayofyear.values
def synth_station(lat):
season = 6*np.sin(2*np.pi*(doy-1)/365.0)
lat_grad = 0.2*(10-lat)
t = 24 + season + lat_grad + rng.normal(0,1.2,len(dates))
rain = rng.gamma(1.3, np.clip(2 + 2*np.sin(2*np.pi*(doy-30)/365.0) + 1.3*np.sin(4*np.pi*(doy-30)/365.0),0.2,None), len(dates))
rain = np.maximum(rain-2.0, 0.0)
return t, rain
rows = []
for sid,name,lat,lon in stations:
t, p = synth_station(lat)
rows.append(pd.DataFrame({"date":dates,"station_id":sid,"name":name,"lat":lat,"lon":lon,"t2m_c":t,"precip_mm":p}))
df = pd.concat(rows, ignore_index=True).set_index("date").sort_index()
# Grid for maps/Hovmöller
lat = np.arange(-10,16,1.0)
lon = np.arange(30,51,1.0)
time = np.arange(366)
seasonal = 5*np.sin(2*np.pi*time/365.0)[:,None,None]
lat_grad = (24.0 - 0.25*lat)[None,:,None]
noise = rng.normal(0.0,0.7,size=(time.size,lat.size,lon.size))
t2m_grid = 24.0 + seasonal + lat_grad + noise
Basics: Figure, Axes, Line Plot¶
plt.figure() # Create a new figure
plt.plot(domain_mean) # Plot the domain-mean T2m
plt.title("Domain-mean T2m") # Add a title
plt.xlabel("Day of year") # Label x-axis
plt.ylabel("°C") # Label y-axis
plt.grid(True) # Add a grid
plt.tight_layout() # Adjust layout
plt.show() # Show the plot
Line Plots (Time Series)¶
date
2020-01-01 24.195476
2020-01-02 24.655775
2020-01-03 24.071564
2020-01-04 23.435008
2020-01-05 24.061210
...
2020-12-27 26.026236
2020-12-28 24.594649
2020-12-29 24.054443
2020-12-30 22.067378
2020-12-31 24.659549
Name: t2m_c, Length: 366, dtype: float64
plt.figure() # Create a new figure
plt.plot(add.index, add.values) # Plot time series
plt.title("ADD daily T2m (°C)") # Add title
plt.xlabel("Date") # Label x-axis
plt.ylabel("°C") # Label y-axis
plt.grid(True) # Add grid
plt.tight_layout() # Adjust layout
plt.show() # Show the plot
Rolling mean¶
plt.figure() # Create a new figure
plt.plot(add.index, add.rolling(7).mean()) # Plot 7-day running mean
plt.title("ADD 7-day Running Mean") # Add title
plt.xlabel("Date") # Add x-label
plt.ylabel("°C") # Add y-label
plt.grid(True) # Add grid
plt.tight_layout() # Adjust layout
plt.show() # Show the plot
Scatter Plot¶
station_id name lat lon t2m_c precip_mm
date
2020-01-01 ADD Addis Ababa 9.03 38.74 24.195476 0.0
2020-01-01 MOG Mogadishu 2.05 45.32 23.299703 0.0
2020-01-01 NBO Nairobi -1.29 36.82 26.105206 0.0
2020-01-02 ADD Addis Ababa 9.03 38.74 24.655775 0.0
2020-01-02 MOG Mogadishu 2.05 45.32 25.503162 0.0
... ... ... ... ... ... ...
2020-12-30 ADD Addis Ababa 9.03 38.74 22.067378 0.0
2020-12-30 NBO Nairobi -1.29 36.82 25.066196 0.0
2020-12-31 NBO Nairobi -1.29 36.82 26.596805 0.0
2020-12-31 ADD Addis Ababa 9.03 38.74 24.659549 0.0
2020-12-31 MOG Mogadishu 2.05 45.32 23.772085 0.0
[1098 rows x 6 columns]
station_id name lat lon t2m_c precip_mm
date
2020-01-01 NBO Nairobi -1.29 36.82 26.105206 0.0
2020-01-02 NBO Nairobi -1.29 36.82 25.991913 0.0
2020-01-03 NBO Nairobi -1.29 36.82 27.375470 0.0
2020-01-04 NBO Nairobi -1.29 36.82 25.266630 0.0
2020-01-05 NBO Nairobi -1.29 36.82 28.279682 0.0
... ... ... ... ... ... ...
2020-12-27 NBO Nairobi -1.29 36.82 26.749515 0.0
2020-12-28 NBO Nairobi -1.29 36.82 26.731095 0.0
2020-12-29 NBO Nairobi -1.29 36.82 25.358559 0.0
2020-12-30 NBO Nairobi -1.29 36.82 25.066196 0.0
2020-12-31 NBO Nairobi -1.29 36.82 26.596805 0.0
[366 rows x 6 columns]
plt.figure() # Create a new figure
plt.scatter(nbo["t2m_c"], nbo["precip_mm"], alpha=0.5) # Scatter plot
plt.title("NBO: Daily T vs Precip") # Add title
plt.xlabel("T2m (°C)") # Add x-label
plt.ylabel("Precip (mm)") # Add y-label
plt.grid(True) # Add grid
plt.tight_layout() # Adjust layout
plt.show() # Show the plot
Histogram¶
date
2020-01-01 0.0
2020-01-02 0.0
2020-01-03 0.0
2020-01-04 0.0
2020-01-05 0.0
...
2020-12-27 0.0
2020-12-28 0.0
2020-12-29 0.0
2020-12-30 0.0
2020-12-31 0.0
Name: precip_mm, Length: 366, dtype: float64
plt.figure() # Create a new figure
plt.hist(nbo_precip, bins=20) # Histogram of precip data
plt.title("NBO: Precipitation Distribution") # Add title
plt.xlabel("mm/day") # Add x-label
plt.ylabel("Count") # Add y-lable
plt.grid(True) # Add grid
plt.tight_layout() # Adjust layout
plt.show() # Show the plot
Bar Charts¶
monthly = (df.groupby(["station_id", pd.Grouper(freq="MS")])
.agg(t2m_c_mean=("t2m_c","mean"), precip_mm_sum=("precip_mm","sum"))
.reset_index())
monthly.head()
station_id date t2m_c_mean precip_mm_sum
0 ADD 2020-01-01 25.167938 18.492305
1 ADD 2020-02-01 28.404934 34.949877
2 ADD 2020-03-01 29.814355 57.472656
3 ADD 2020-04-01 29.834072 93.142862
4 ADD 2020-05-01 28.173009 100.644649
station_id date t2m_c_mean precip_mm_sum
0 ADD 2020-01-01 25.167938 18.492305
1 ADD 2020-02-01 28.404934 34.949877
2 ADD 2020-03-01 29.814355 57.472656
3 ADD 2020-04-01 29.834072 93.142862
4 ADD 2020-05-01 28.173009 100.644649
Vertical Bar Chart¶
plt.figure() # Create a new figure
plt.bar(mon_add["date"].dt.strftime("%b"), mon_add["precip_mm_sum"].values) # Bar plot
plt.title("ADD: Monthly Precipitation Totals") # Add title
plt.xlabel("Month") # Add x-label
plt.ylabel("mm") # Add y-label
plt.tight_layout() # Adjust layout
plt.show() # Show the plot
Horizontal Bar Chart¶
plt.figure() # Create a new figure
plt.barh(mon_add["date"].dt.strftime("%b"), mon_add["precip_mm_sum"].values) # Bar plot
plt.title("ADD: Monthly Precipitation Totals") # Add title
plt.xlabel("Month") # Add x-label
plt.ylabel("mm") # Add y-label
plt.tight_layout() # Adjust layout
plt.show() # Show the plot
Box Plot¶
station_id name lat lon t2m_c precip_mm
date
2020-01-01 ADD Addis Ababa 9.03 38.74 24.195476 0.0
2020-01-02 ADD Addis Ababa 9.03 38.74 24.655775 0.0
2020-01-03 ADD Addis Ababa 9.03 38.74 24.071564 0.0
2020-01-04 ADD Addis Ababa 9.03 38.74 23.435008 0.0
2020-01-05 ADD Addis Ababa 9.03 38.74 24.061210 0.0
station_id name lat lon t2m_c precip_mm month
date
2020-01-01 ADD Addis Ababa 9.03 38.74 24.195476 0.0 1
2020-01-02 ADD Addis Ababa 9.03 38.74 24.655775 0.0 1
2020-01-03 ADD Addis Ababa 9.03 38.74 24.071564 0.0 1
2020-01-04 ADD Addis Ababa 9.03 38.74 23.435008 0.0 1
2020-01-05 ADD Addis Ababa 9.03 38.74 24.061210 0.0 1
station_id name lat lon t2m_c precip_mm month
date
2020-12-27 ADD Addis Ababa 9.03 38.74 26.026236 0.0 12
2020-12-28 ADD Addis Ababa 9.03 38.74 24.594649 0.0 12
2020-12-29 ADD Addis Ababa 9.03 38.74 24.054443 0.0 12
2020-12-30 ADD Addis Ababa 9.03 38.74 22.067378 0.0 12
2020-12-31 ADD Addis Ababa 9.03 38.74 24.659549 0.0 12
data = [add_df.loc[add_df["month"]==m, "t2m_c"].values for m in range(1,13)]
data # extracted data for each month
[array([24.19547618, 24.65577478, 24.07156424, 23.4350078 , 24.06120962,
23.51981293, 24.88478251, 26.52350656, 24.42702481, 24.37528304,
25.80956933, 25.75162467, 25.5511241 , 24.40896742, 25.59093441,
26.5604836 , 24.21269149, 25.37575568, 23.74206006, 24.57417268,
24.00905535, 26.03394738, 24.89141148, 26.83349767, 26.79102924,
26.47284448, 23.7705419 , 26.23694458, 26.91710049, 27.20237382,
25.32049038]),
array([26.67272173, 26.16027075, 26.45142634, 28.78169023, 26.62516893,
27.63977559, 28.82362796, 27.1445407 , 27.79223815, 28.13915078,
28.1623155 , 26.69374072, 28.33202999, 29.94690471, 26.53417635,
29.49519896, 28.67908906, 27.83673766, 31.07633238, 29.65854651,
27.37134228, 28.96519458, 29.63170144, 28.77562798, 29.88272667,
29.04303854, 29.98172083, 30.96393754, 28.48210785]),
array([29.59035008, 28.84276904, 29.60207232, 28.07377163, 28.85072616,
29.35635005, 30.71459952, 31.05303434, 28.13159438, 28.80567926,
30.57330748, 27.44223261, 29.31177449, 29.78359479, 31.43982693,
30.78806338, 29.59578352, 29.57209246, 29.73836825, 31.88932533,
29.56820889, 29.73642643, 30.54120354, 29.98867044, 29.91059218,
28.82242687, 30.15568822, 29.64565237, 31.58396586, 30.97320627,
30.16363868]),
array([30.99600167, 29.78565649, 31.45382931, 30.18079882, 30.88156179,
28.62488152, 30.58064793, 28.12769615, 27.69828965, 29.76069329,
29.02977577, 30.28842267, 32.7653901 , 29.05199549, 29.27797688,
30.24811143, 30.56643924, 29.73461962, 29.66896958, 30.72712503,
30.47445602, 28.57487708, 29.683326 , 29.78208855, 28.43412021,
29.9694347 , 28.58458704, 30.73551579, 29.75363903, 29.58124101]),
array([28.71500389, 29.23052098, 26.92263747, 27.90780538, 29.64496064,
26.59784474, 30.10915853, 26.93756391, 29.87918339, 27.89330269,
29.77809355, 28.93447313, 26.86579904, 30.14031056, 30.30139391,
28.42109559, 28.09880213, 28.161847 , 27.10845767, 29.5206873 ,
27.47346783, 27.98489978, 27.0146212 , 27.13441988, 26.2704742 ,
29.22919469, 27.45172852, 28.71064152, 27.48143215, 26.54509606,
26.89835203]),
array([26.52917362, 27.12118716, 26.57063684, 26.5695341 , 25.18282309,
25.77578693, 28.63500481, 25.7500618 , 25.19533525, 26.76907398,
27.95640052, 24.42562173, 25.82241603, 25.21581385, 23.76216068,
26.65790501, 25.64800038, 25.66156423, 24.57235173, 25.91977833,
24.62544269, 24.99935823, 23.73888223, 23.50716575, 26.46659227,
24.15269485, 25.0083353 , 24.51473114, 23.92275915, 23.73935748]),
array([25.00174109, 23.78011689, 23.85735698, 23.96253247, 25.24453032,
24.54629429, 24.08589982, 22.84776029, 21.76314847, 24.45864074,
24.37689448, 22.94655041, 23.66422327, 23.85060617, 23.80958732,
23.81752709, 22.06526586, 24.33058353, 20.91781141, 23.34942545,
22.8102676 , 23.16866906, 24.27852758, 23.70908678, 20.45824605,
19.71145835, 22.62426483, 20.33287239, 21.44367337, 22.37471593,
19.30380694]),
array([18.65457257, 21.40878448, 21.06288278, 20.62759368, 20.88270614,
19.71874766, 18.8510952 , 20.3842698 , 19.33624197, 18.44924553,
20.94848901, 20.1893835 , 20.67345106, 18.92220561, 19.24464209,
18.76164671, 18.82396828, 20.05114418, 18.8070984 , 20.10527858,
20.01837543, 21.97488894, 17.80875401, 20.48238567, 19.24773477,
19.27793602, 17.49603822, 18.62621455, 20.01434237, 18.96909294,
19.11241248]),
array([18.61490207, 20.29941047, 18.83988898, 16.17846545, 17.94337813,
16.36781629, 14.78677215, 18.01211725, 20.20992823, 18.62925581,
17.13037829, 17.37499068, 19.8286499 , 18.63084505, 18.4707758 ,
18.32221916, 18.40737113, 19.30442347, 18.97939861, 18.55528633,
17.02686907, 18.87526498, 17.42621912, 19.54707372, 16.69810715,
18.04890142, 18.19766709, 16.61114705, 20.26308829, 19.94698817]),
array([17.63775505, 19.12145439, 18.65291122, 15.06711612, 18.51052778,
18.14487313, 18.32855338, 16.94841684, 17.93118857, 18.05599283,
19.71287016, 18.70747365, 18.32024391, 20.18416127, 17.70731895,
17.93224401, 16.24711528, 20.33951806, 19.64485974, 19.62063933,
19.35754179, 18.72315493, 18.88733663, 18.36576437, 18.46491156,
18.81708146, 20.6104102 , 19.5089374 , 18.81944279, 18.24337931,
18.22726369]),
array([20.96466226, 19.70312222, 19.23135024, 18.79155803, 17.93427659,
19.24453178, 20.43422311, 18.97728136, 19.23953379, 19.31226982,
19.77567839, 17.80055442, 19.49901984, 18.82686115, 20.98556231,
19.07253163, 20.76413949, 21.97666323, 19.84787678, 19.58033113,
20.61112491, 20.45928468, 19.35079916, 21.17874318, 23.12781762,
20.48405507, 20.63593669, 19.71205054, 21.4364125 , 19.64560287]),
array([19.90302947, 22.85719769, 20.3261547 , 22.80225303, 23.42657605,
22.00203013, 22.44914175, 24.22272069, 21.73957866, 21.36033631,
20.54498532, 22.31643053, 24.13944571, 23.61461986, 21.43174157,
21.63542977, 22.15695793, 23.21319262, 22.71699929, 23.32198448,
23.52232839, 22.90961877, 23.32211864, 23.72066258, 23.47462617,
24.28243625, 26.02623637, 24.59464874, 24.05444299, 22.06737752,
24.6595485 ])]
plt.figure() # Create a new figure
plt.boxplot(data, tick_labels=["Jan","Feb","Mar","Apr","May","Jun","Jul","Aug","Sep","Oct","Nov","Dec"], showfliers=False) # Boxplot
plt.title("ADD: Monthly Distribution of Daily T2m") # Add title
plt.ylabel("°C") # Add y-label
plt.tight_layout() # Adjust layout
plt.show() # Show the plot
Heatmaps (Hovmöller and Map)¶
plt.figure() # Create a new figure
plt.imshow(time_lat.T, # Transpose the data for correct orientation
aspect="auto", # Maintain aspect ratio
origin="lower", # Set the origin to the lower left corner
extent=[0, time_lat.shape[0], lat.min(), lat.max()]) # Set the extent
plt.title("Hovmöller: T2m (time vs lat)") # Add title
plt.xlabel("Day of year") # Add x-label
plt.ylabel("Latitude") # Add y-label
plt.colorbar(label="°C") # Add colorbar
plt.tight_layout() # Adjust layout
plt.show() # Show the plot
# Single-day map
d = 200
plt.figure() # Create a new figure
plt.imshow(t2m_grid[d], # Data for day d
origin="lower", # Set origin to lower left corner
extent=[lon.min(), lon.max(), lat.min(), lat.max()], # Set extent
aspect="auto")
plt.title(f"Daily T2m map (day {d})") # Add title
plt.xlabel("Longitude") # Add x-label
plt.ylabel("Latitude") # Add y-label
plt.colorbar(label="°C") # Add colorbar
plt.tight_layout() # Adjust layout
plt.show() # Show the plot
Dual Axes (Temperature vs Precip)¶
add_mon = monthly[monthly["station_id"]=="ADD"].set_index("date") # Extract Addis Ababa monthly data
add_mon.head()
station_id t2m_c_mean precip_mm_sum
date
2020-01-01 ADD 25.167938 18.492305
2020-02-01 ADD 28.404934 34.949877
2020-03-01 ADD 29.814355 57.472656
2020-04-01 ADD 29.834072 93.142862
2020-05-01 ADD 28.173009 100.644649
plt.figure() # Create a new figure
ax1 = plt.gca() # Get current axes
ax1.plot(add_mon.index, add_mon["t2m_c_mean"]) # Plot T2m mean
ax1.set_xlabel("Month") # Set x-label
ax1.set_ylabel("T2m (°C)") # Set y-label
ax1.grid(True) # Add grid
ax2 = ax1.twinx() # Create a twin Axes sharing the x-axis
ax2.bar(add_mon.index, add_mon["precip_mm_sum"], alpha=0.8, width=8.0) # Bar plot for precip
ax2.set_ylabel("Precip (mm)") # Set y-label for precip
plt.title("ADD: Monthly T vs Precip (dual axis)") # Add title
plt.tight_layout() # Adjust layout
plt.show() # Show the plot
Styling & Customization¶
date
2020-01-01 NaN
2020-01-02 NaN
2020-01-03 NaN
2020-01-04 NaN
2020-01-05 NaN
...
2020-12-27 25.336285
2020-12-28 25.459719
2020-12-29 25.474082
2020-12-30 25.600557
2020-12-31 25.811693
Name: t2m_c, Length: 366, dtype: float64
plt.figure() # Create a new figure
plt.plot(series.index, series.values, label="NBO 7-day mean") # Plot the series
plt.title("NBO Temperature (smoothed)") # Add title
plt.xlabel("Date") # Add x-label
plt.ylabel("°C") # Add y-label
plt.grid(True) # Add grid
plt.legend() # Add legend
plt.xticks(rotation=30) # Rotate x-ticks
plt.tight_layout() # Adjust layout
plt.show() # Show plot
Subplots/panel Plot¶
Basic grid of subplots¶
fig, axes = plt.subplots(nrows=1, ncols=2, # Create subplots
figsize=(12, 3), # Set figure size
sharex=True, # Share x-axis
constrained_layout=True) # Constrained layout
for ax, sid, title in zip(axes, ["ADD","NBO"], ["Addis Ababa","Nairobi"]): # Loop over axes and stations
s = df[df["station_id"]==sid]["t2m_c"] # Extract station T2m
ax.plot(s.index, s.values, linewidth=1.2) # Plot T2m
ax.set_title(f"{title}: Daily T2m") # Set title
ax.set_ylabel("°C") # Set y-label
ax.grid(True) # Add grid
axes[1].set_xlabel("Date") # Set x-label for the last subplot
plt.show() # Show the plot
Mixed 2×2 panel: line, histogram, scatter, monthly bars¶
fig, axes = plt.subplots(2, 2, figsize=(10, 6), constrained_layout=True)
# (1,1) Line: ADD daily T
add = df[df["station_id"]=="ADD"]["t2m_c"]
axes[0,0].plot(add.index, add.values)
axes[0,0].set_title("ADD Daily T2m")
axes[0,0].set_ylabel("°C")
axes[0,0].grid(True)
# (1,2) Histogram: MOG precip
mog_pr = df[df["station_id"]=="MOG"]["precip_mm"]
axes[0,1].hist(mog_pr, bins=30)
axes[0,1].set_title("MOG Precip Distribution")
axes[0,1].set_xlabel("mm/day")
axes[0,1].set_ylabel("Count")
axes[0,1].grid(True)
# (2,1) Scatter: NBO T vs precip
nbo = df[df["station_id"]=="NBO"]
axes[1,0].scatter(nbo["t2m_c"], nbo["precip_mm"], alpha=0.5)
axes[1,0].set_title("NBO: T vs Precip")
axes[1,0].set_xlabel("T2m (°C)")
axes[1,0].set_ylabel("mm/day")
axes[1,0].grid(True)
# (2,2) Monthly bars: ADD precip
mon_add = monthly[monthly["station_id"]=="ADD"]
axes[1,1].bar(mon_add["date"].dt.strftime("%b"), mon_add["precip_mm_sum"].values)
axes[1,1].set_title("ADD Monthly Precip")
axes[1,1].set_xlabel("Month")
axes[1,1].set_ylabel("mm")
fig.suptitle("Climate Quicklook (2×2)", fontsize=14)
plt.show()
Stacked layout with GridSpec¶
from matplotlib.gridspec import GridSpec # Import GridSpec
add = df[df["station_id"]=="ADD"]
fig = plt.figure(figsize=(10, 5),
constrained_layout=True)
gs = GridSpec(nrows=3, ncols=1, height_ratios=[2, 1, 0.05], figure=fig)
ax1 = fig.add_subplot(gs[0,0])
ax2 = fig.add_subplot(gs[1,0],
sharex=ax1)
# Top: daily T and 30-day running mean
ax1.plot(add.index, add["t2m_c"], alpha=0.4, label="Daily")
ax1.plot(add.index, add["t2m_c"].rolling(30).mean(), linewidth=2, label="30-day mean")
ax1.set_title("ADD Temperature")
ax1.set_ylabel("°C")
ax1.grid(True)
ax1.legend(loc="upper right")
# Bottom: daily precip bars
ax2.bar(add.index, add["precip_mm"], width=1.0)
ax2.set_title("ADD Precipitation")
ax2.set_ylabel("mm/day")
ax2.set_xlabel("Date")
ax2.grid(True, axis="y")
plt.setp(ax1.get_xticklabels(), visible=False) # keep top clean
plt.show()
Saving Figures¶
plt.figure(figsize=(10, 6)) # Create a new figure
plt.plot(add.index[:60], add.values[:60]) # Plot first 60 days
plt.title("Example to Save") # Add title
plt.xlabel("Date") # Add x-label
plt.ylabel("°C") # Add y-label
plt.grid(True) # Add grid
plt.savefig("data/processed/example_plot.png", dpi=300, bbox_inches="tight") # Save as PNG
plt.savefig("data/processed/example_plot.pdf", dpi=300, bbox_inches="tight") # Save as PDF
plt.savefig("data/processed/example_plot.svg", dpi=300, bbox_inches="tight") # Save as SVG
plt.savefig("data/processed/example_plot.eps", dpi=300, bbox_inches="tight") # Save as EPS
plt.tight_layout() # Adjust layout
plt.show() # Show plot
📝 Test Your Knowledge¶
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📚 Summary¶
In this tutorial, you've learned:
✅ Matplotlib Basics - Figure, axes, and plot creation
✅ Plot Types - Line plots, scatter plots, histograms, bar charts, box plots
✅ Heatmaps - Hovmöller diagrams and spatial maps
✅ Customization - Titles, labels, legends, grids, and styles
✅ Advanced Layouts - Subplots, dual axes, and GridSpec
✅ Saving Figures - Multiple formats (PNG, PDF, SVG, EPS)
