I want to combine all 3 seaborn scatter plots under one "frame".
plt.figure(figsize=(7,15))
plt.subplots(3,1)
sns.scatterplot(x=train['Garage Area'], y=train['SalePrice'])
plt.show()
sns.scatterplot(x=train['Gr Liv Area'], y=train['SalePrice'])
plt.show()
sns.scatterplot(x=train['Overall Cond'], y=train['SalePrice'])
plt.show()
But it creates 5, the first 3 are small according to (7,15) size but the last 2 are different.
I suspect it should be
plt.figure(figsize=(7,15))
fig,ax = plt.subplots(3,1)
ax[0] = fig.add_subplot(sns.scatterplot(x=train['Garage Area'], y=train['SalePrice']))
#plt.show()
ax[1] = fig.add_subplot(sns.scatterplot(x=train['Gr Liv Area'], y=train['SalePrice']))
#plt.show()
ax[2] =fig.add_subplot(sns.scatterplot(x=train['Overall Cond'], y=train['SalePrice']))
plt.show()
but all 3 plots are stuck in the last 3rd chart!
The following is one way to do it:
Create a figure with 3 subplots (3 rows, 1 column)
Pass the respective subplot using ax[0], ax[1] and ax[2] to the three separate sns.scatterplot commands using the keyword ax
fig, ax = plt.subplots(3, 1, figsize=(7,15))
sns.scatterplot(x=train['Garage Area'], y=train['SalePrice'], ax=ax[0])
sns.scatterplot(x=train['Gr Liv Area'], y=train['SalePrice'], ax=ax[1])
sns.scatterplot(x=train['Overall Cond'], y=train['SalePrice'], ax=ax[2])
plt.show()
Related
This question already has answers here:
Annotate bars with values on Pandas bar plots
(4 answers)
Closed 1 year ago.
I would like to create an annotation to a bar chart that compares the value of the bar to two reference values. An overlay such as shown in the picture, a kind of staff gauge, is possible, but I'm open to more elegant solutions.
The bar chart is generated with the pandas API to matplotlib (e.g. data.plot(kind="bar")), so a plus would be if the solution is playing nicely with that.
You may use smaller bars for the target and benchmark indicators. Pandas cannot annotate bars automatically, but you can simply loop over the values and use matplotlib's pyplot.annotate instead.
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
a = np.random.randint(5,15, size=5)
t = (a+np.random.normal(size=len(a))*2).round(2)
b = (a+np.random.normal(size=len(a))*2).round(2)
df = pd.DataFrame({"a":a, "t":t, "b":b})
fig, ax = plt.subplots()
df["a"].plot(kind='bar', ax=ax, legend=True)
df["b"].plot(kind='bar', position=0., width=0.1, color="lightblue",legend=True, ax=ax)
df["t"].plot(kind='bar', position=1., width=0.1, color="purple", legend=True, ax=ax)
for i, rows in df.iterrows():
plt.annotate(rows["a"], xy=(i, rows["a"]), rotation=0, color="C0")
plt.annotate(rows["b"], xy=(i+0.1, rows["b"]), color="lightblue", rotation=+20, ha="left")
plt.annotate(rows["t"], xy=(i-0.1, rows["t"]), color="purple", rotation=-20, ha="right")
ax.set_xlim(-1,len(df))
plt.show()
There's no direct way to annotate a bar plot (as far as I am aware) Some time ago I needed to annotate one so I wrote this, perhaps you can adapt it to your needs.
import matplotlib.pyplot as plt
import numpy as np
ax = plt.subplot(111)
ax.set_xlim(-0.2, 3.2)
ax.grid(b=True, which='major', color='k', linestyle=':', lw=.5, zorder=1)
# x,y data
x = np.arange(4)
y = np.array([5, 12, 3, 7])
# Define upper y limit leaving space for the text above the bars.
up = max(y) * .03
ax.set_ylim(0, max(y) + 3 * up)
ax.bar(x, y, align='center', width=0.2, color='g', zorder=4)
# Add text to bars
for xi, yi, l in zip(*[x, y, list(map(str, y))]):
ax.text(xi - len(l) * .02, yi + up, l,
bbox=dict(facecolor='w', edgecolor='w', alpha=.5))
ax.set_xticks(x)
ax.set_xticklabels(['text1', 'text2', 'text3', 'text4'])
ax.tick_params(axis='x', which='major', labelsize=12)
plt.show()
Trying to plot linear regression-plot with Seaborn and I am ending up having this:
and under it these empty plots:
I don't need the last 3 small subplots, or at least how to get them plotted correctly, with the main first 3 subplots above?
Here is the code I used:
fig, axes = plt.subplots(3, 1, figsize=(12, 15))
for col, ax in zip(['gross_sqft_thousands','land_sqft_thousands','total_units'], axes.flatten()):
ax.tick_params(axis='x', rotation=85)
ax.set_ylabel(col, fontsize=15)
sns.jointplot(x="sale_price_millions", y=col, data=clean_df, kind='reg', joint_kws={'line_kws':{'color':'cyan'}}, ax=ax)
fig.suptitle('Sale Price vs Continuous Variables', position=(.5,1.02), fontsize=20)
fig.tight_layout()
fig.show()
I want to place 3 plots using subplots. Two plots on the top row and one plot that will occupy the entire second row.
My code creates a gap between the top two plots and the lower plot. How can I correct that?
df_CI
Country China India
1980 5123 8880
1981 6682 8670
1982 3308 8147
1983 1863 7338
1984 1527 5704
fig = plt.figure() # create figure
ax0 = fig.add_subplot(221) # add subplot 1 (2 row, 2 columns, first plot)
ax1 = fig.add_subplot(222) # add subplot 2 (2 row, 2 columns, second plot).
ax2 = fig.add_subplot(313) # a 3 digit number where the hundreds represent nrows, the tens represent ncols
# and the units represent plot_number.
# Subplot 1: Box plot
df_CI.plot(kind='box', color='blue', vert=False, figsize=(20, 20), ax=ax0) # add to subplot 1
ax0.set_title('Box Plots of Immigrants from China and India (1980 - 2013)')
ax0.set_xlabel('Number of Immigrants')
ax0.set_ylabel('Countries')
# Subplot 2: Line plot
df_CI.plot(kind='line', figsize=(20, 20), ax=ax1) # add to subplot 2
ax1.set_title ('Line Plots of Immigrants from China and India (1980 - 2013)')
ax1.set_ylabel('Number of Immigrants')
ax1.set_xlabel('Years')
# Subplot 3: Box plot
df_CI.plot(kind='bar', figsize=(20, 20), ax=ax2) # add to subplot 1
ax0.set_title('Box Plots of Immigrants from China and India (1980 - 2013)')
ax0.set_xlabel('Number of Immigrants')
ax0.set_ylabel('Countries')
plt.show()
I've always found subplots syntax a little difficult.
With these calls
ax0 = fig.add_subplot(221)
ax1 = fig.add_subplot(222)
you're dividing your figure in a 2x2 grid and filling the first row.
ax2 = fig.add_subplot(313)
Now you're dividing it in three rows and filling the last one.
You're basically creating two independent subplot grids, there is no easy way to define how to space subplots from one with respect to the other.
A much easier and pythonic way is using gridspec to create a single finer grid and address it with python slicing.
fig = plt.figure()
gs = mpl.gridspec.GridSpec(2, 2, wspace=0.25, hspace=0.25) # 2x2 grid
ax0 = fig.add_subplot(gs[0, 0]) # first row, first col
ax1 = fig.add_subplot(gs[0, 1]) # first row, second col
ax2 = fig.add_subplot(gs[1, :]) # full second row
And now you can also easily tune spacing with wspace and hspace.
More complex layouts are also a lot easier, it's just the familiar slicing syntax.
fig = plt.figure()
gs = mpl.gridspec.GridSpec(10, 10, wspace=0.25, hspace=0.25)
fig.add_subplot(gs[2:8, 2:8])
fig.add_subplot(gs[0, :])
for i in range(5):
fig.add_subplot(gs[1, (i*2):(i*2+2)])
fig.add_subplot(gs[2:, :2])
fig.add_subplot(gs[8:, 2:4])
fig.add_subplot(gs[8:, 4:9])
fig.add_subplot(gs[2:8, 8])
fig.add_subplot(gs[2:, 9])
fig.add_subplot(gs[3:6, 3:6])
# fancy colors
cmap = mpl.cm.get_cmap("viridis")
naxes = len(fig.axes)
for i, ax in enumerate(fig.axes):
ax.set_xticks([])
ax.set_yticks([])
ax.set_facecolor(cmap(float(i)/(naxes-1)))
I have a subplot that plots a line (x,y) and a particular point (xx,yy). I want to highligh (xx,yy), so I've plotted it with scatter. However, even if I order it after the original plot, the new point still shows up behind the original line. How can I fix this? MWE below.
x = 1:10
y = 1:10
xx = 5
yy = 5
fig, ax = subplots()
ax[:plot](x,y)
ax[:scatter](xx,yy, color="red", label="h_star", s=100)
legend()
xlabel("x")
ylabel("y")
title("test")
grid("on")
You can change which plots are displayed on top of each other with the argument zorder. The matplotlib example shown here gives a brief explanation:
The default drawing order for axes is patches, lines, text. This
order is determined by the zorder attribute. The following defaults
are set
Artist Z-order
Patch / PatchCollection 1
Line2D / LineCollection 2
Text 3
You can change the order for individual artists by setting the zorder.
Any individual plot() call can set a value for the zorder of that
particular item.
A full example based on the code in the question, using python is shown below:
import matplotlib.pyplot as plt
x = range(1,10)
y = range(1,10)
xx = 5
yy = 5
fig, ax = plt.subplots()
ax.plot(x,y)
# could set zorder very high, say 10, to "make sure" it will be on the top
ax.scatter(xx,yy, color="red", label="h_star", s=100, zorder=3)
plt.legend()
plt.xlabel("x")
plt.ylabel("y")
plt.title("test")
plt.grid("on")
plt.show()
Which gives:
I have 2 dataframes. I want to plot a histogram based on a column 'rate' for each, side by side. How to do it?
I tried this:
import matplotlib.pyplot as plt
plt.subplot(1,2,1)
dflux.hist('rate' , bins=100)
plt.subplot(1,2,2)
dflux2.hist('rate' , bins=100)
plt.tight_layout()
plt.show()
It did not have the desired effect. It showed two blank charts then one populated chart.
Use subplots to define a figure with two axes. Then specify the axis to plot to within hist using the ax parameter.
fig, axes = plt.subplots(1, 2)
dflux.hist('rate', bins=100, ax=axes[0])
dflux2.hist('rate', bins=100, ax=axes[1])
Demo
dflux = pd.DataFrame(dict(rate=np.random.randn(10000)))
dflux2 = pd.DataFrame(dict(rate=np.random.randn(10000)))
fig, axes = plt.subplots(1, 2)
dflux.hist('rate', bins=100, ax=axes[0])
dflux2.hist('rate', bins=100, ax=axes[1])