I'm trying to visualize correlations using a heatmap in matplotlib (1.4.3), which works fine. I'd like to highlight specific cells/points in the heatmap, and my first guess was to overlay a second plot that creates the highlights. As imshow creates a new window, this does not work as intended, though. A condensed version of my code is below. Is there another way to render something matrix-like on top of an existing figure?
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
df = pd.DataFrame(np.random.rand(4, 4), columns=list('ABCD'))
corrmatrix = df.corr()
fig, ax = plt.subplots()
im = ax.imshow(corrmatrix, cmap='afmhot', interpolation='none')
plt.colorbar(im)
ax.set_xticks(np.arange(len(df.columns)))
ax.set_xticklabels(df.columns)
ax.set_yticks(np.arange(len(df.columns)))
ax.set_yticklabels(df.columns)
relevant_cells = df > 0.9
rel_ax = ax.imshow(relevant_cells, cmap='YlOrBr', interpolation='none')
plt.show()
Emphasis can be achieved by overlaying the two heatmaps and adjusting them by transparency. The color map has been intentionally changed for clarity: if C,C and A,C is True
rel_ax = ax.imshow(relevant_cells, cmap='Blues', interpolation='none', alpha=0.7)
Related
I want to specify the color of the area surrounding a plot created using the df.plot() in Pandas/Python.
Using .set_facecolor as in the code below only changes the area inside the axes (see image), I want to change the color outside too.
import pandas as pd
import numpy as np
df = pd.DataFrame(components, columns=['PC1','PC2']
df.plot('PC1','PC2','scatter').set_facecolor('green')
Replacing the last line with these two lines produces the same graph.
ax = df.plot('PC1','PC2','scatter')
ax.set_facecolor('green')
setfacecolor example
IIUC, you can use fig.set_facecolor:
fig, ax = plt.subplots()
df.plot('PC1','PC2','scatter', ax=ax).set_facecolor('green')
fig.set_facecolor('green')
plt.show()
Output:
I have the following problem: I'm trying to overlay two plots: One Pandas plot via plot.area() for a dataframe, and a second plot that is a standard Matplotlib plot. Depending the coder order for those two, the Matplotlib plot is displayed only if the code is before the Pandas plot.area() on the same axes.
Example: I have a Pandas dataframe called revenue that has a DateTimeIndex, and a single column with "revenue" values (float). Separately I have a dataset called projection with data along the same index (revenue.index)
If the code looks like this:
import pandas as pd
import matplotlib.pyplot as plt
fig, ax = plt.subplots(figsize=(10, 6))
# First -- Pandas area plot
revenue.plot.area(ax = ax)
# Second -- Matplotlib line plot
ax.plot(revenue.index, projection, color='black', linewidth=3)
plt.tight_layout()
plt.show()
Then the only thing displayed is the pandas plot.area() like this:
1/ Pandas plot.area() and 2/ Matplotlib line plot
However, if the order of the plotting is reversed:
fig, ax = plt.subplots(figsize=(10, 6))
# First -- Matplotlib line plot
ax.plot(revenue.index, projection, color='black', linewidth=3)
# Second -- Pandas area plot
revenue.plot.area(ax = ax)
plt.tight_layout()
plt.show()
Then the plots are overlayed properly, like this:
1/ Matplotlib line plot and 2/ Pandas plot.area()
Can someone please explain me what I'm doing wrong / what do I need to do to make the code more robust ? Kind TIA.
The values on the x-axis are different in both plots. I think DataFrame.plot.area() formats the DateTimeIndex in a pretty way, which is not compatible with pyplot.plot().
If you plot of the projection first, plot.area() can still plot the data and does not format the x-axis.
Mixing the two seems tricky to me, so I would either use pyplot or Dataframe.plot for both the area and the line:
import pandas as pd
from matplotlib import pyplot as plt
projection = [1000, 2000, 3000, 4000]
datetime_series = pd.to_datetime(["2021-12","2022-01", "2022-02", "2022-03"])
datetime_index = pd.DatetimeIndex(datetime_series.values)
revenue = pd.DataFrame({"value": [1200, 2200, 2800, 4100]})
revenue = revenue.set_index(datetime_index)
fig, ax = plt.subplots(1, 2, figsize=(10, 4))
# Option 1: only pyplot
ax[0].fill_between(revenue.index, revenue.value)
ax[0].plot(revenue.index, projection, color='black', linewidth=3)
ax[0].set_title("Pyplot")
# Option 2: only DataFrame.plot
revenue["projection"] = projection
revenue.plot.area(y='value', ax=ax[1])
revenue.plot.line(y='projection', ax=ax[1], color='black', linewidth=3)
ax[1].set_title("DataFrame.plot")
The results then look like this, where DataFrame.plot gives a much cleaner looking result:
If you do not want the projection in the revenue DataFrame, you can put it in a separate DataFrame and set the index to match revenue:
projection_df = pd.DataFrame({"projection": projection})
projection_df = projection_df.set_index(datetime_index)
projection_df.plot.line(ax=ax[1], color='black', linewidth=3)
I am having an issue trying to superimpose plots with seaborn. I am able to generate the two plots separetly as
fig, (ax1,ax2) = plt.subplots(ncols=2,figsize=(30, 7))
sns.lineplot(data=data1, y='MSE',x='pct_gc',ax=ax1)
sns.boxplot(x="pct_gc", y="MSE", data=data2,ax=ax2,width=0.4)
The output looks like this:
But when i try to put both plots superimposed, but assiging both to the same ax object.
fig, (ax1,ax2) = plt.subplots(ncols=2,figsize=(30, 7))
sns.lineplot(data=data1, y='MSE',x='pct_gc',ax=ax1)
sns.boxplot(x="pct_gc", y="MSE", data=data2,ax=ax2,width=0.4)
I am not able to identify with the X axis in the Lineplot changes when superimposing both plots (both plots X axis go from 0 to 0.069).
My goal is for both plots to be superimposed, while keeping the same X axis range.
Seaborn's boxplot creates categorical x-axis, with all boxes nicely with the same distance. Internally the x-axis is numbered as 0, 1, 2, ... but externally it gets the labels from 0 to 0.069.
To combine a line plot with a boxplot, matplotlib's boxplot can be addressed directly, so that positions and widths can be set explicitly. When patch_artist=True, a rectangle is created (instead of just lines), for which a facecolor can be given. manage_ticks=False prevents that boxplot changes the x ticks and their limits. Optionally notch=True would accentuate the median a bit more, but depending on the data, the confidence interval might be too large and look weird.
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import seaborn as sns
data1 = pd.DataFrame({'pct_gc': np.linspace(0, 0.069, 200), 'MSE': np.random.normal(0.02, 0.1, 200).cumsum()})
data1['pct_range'] = pd.cut(data1['pct_gc'], 10)
fig, ax1 = plt.subplots(ncols=1, figsize=(20, 7))
sns.lineplot(data=data1, y='MSE', x='pct_gc', ax=ax1)
for interval, color in zip(np.unique(data1['pct_range']), plt.cm.tab10.colors):
ax1.boxplot(data1[data1['pct_range'] == interval]['MSE'],
positions=[interval.mid], widths=0.4 * interval.length,
patch_artist=True, boxprops={'facecolor': color},
notch=False, medianprops={'color':'yellow', 'linewidth':2},
manage_ticks=False)
plt.show()
I have seaborn heatmap and I would like to plot a lineplot on top of it while using the same x and y axis that the heatmap is using.
I expected the line to behave like in this post and take up most of the space of the heatmap, but instead the output I got was the following plot where it only occupied a small section of the heatmap. How can I make the line take up most of the space in the heatmap?
Below is the minimal working example that produced the plot I linked above.
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
import pandas as pd
num = 11
a = np.eye(num)
x = np.round(np.linspace(0, 1, num=num), 1)
y = np.round(np.linspace(0, 1, num=num), 1)
df = pd.DataFrame(a, columns=x, index=y)
f, ax = plt.subplots()
ax = sns.heatmap(df, cbar=False)
ax.axes.invert_yaxis()
sns.lineplot(x=x, y=y)
plt.show()
Perhaps just a simple fix here:
sns.lineplot(x=x*num, y=y*num)
I am working on a task called knowledge tracing which estimates the student mastery level over time. I would like to plot a similar figure as below using the Matplotlib or Seaborn.
It uses different colors to represent a knowledge concept, instead of a text. However, I have googled and found there is no article is talking about how we can do this.
I tried the following
# simulate a record of student mastery level
student_mastery = np.random.rand(5, 30)
df = pd.DataFrame(student_mastery)
# plot the heatmap using seaborn
marker = matplotlib.markers.MarkerStyle(marker='o', fillstyle='full')
sns_plot = sns.heatmap(df, cmap="RdYlGn", vmin=0.0, vmax=1.0)
y_limit = 5
y_labels = [marker for i in range(y_limit)]
plt.yticks(range(y_limit), y_labels)
Yet it simply returns the __repr__ of the marker, e.g., <matplotlib.markers.MarkerStyle at 0x1c5bb07860> on the yticks.
Thanks in advance!
While How can I make the xtick labels of a plot be simple drawings using matplotlib? gives you a general solution for arbitrary shapes, for the shapes shown here, it may make sense to use unicode symbols as text and colorize them according to your needs.
import matplotlib.pyplot as plt
import numpy as np; np.random.seed(42)
fig, ax = plt.subplots()
ax.imshow(np.random.rand(3,10), cmap="Greys")
symbolsx = ["⚪", "⚪", "⚫", "⚫", "⚪", "⚫","⚪", "⚫", "⚫","⚪"]
colorsx = np.random.choice(["#3ba1ab", "#b43232", "#8ecc3a", "#893bab"], 10)
ax.set_xticks(range(len(symbolsx)))
ax.set_xticklabels(symbolsx, size=40)
for tick, color in zip(ax.get_xticklabels(), colorsx):
tick.set_color(color)
symbolsy = ["◾", "◾", "◾"]
ax.set_yticks(range(len(symbolsy)))
ax.set_yticklabels(symbolsy, size=40)
for tick, color in zip(ax.get_yticklabels(), ["crimson", "gold", "indigo"]):
tick.set_color(color)
plt.show()