The following code generates the linked image. It generates mostly what I want but I would like the box color to be different between Real and Preds. How would I do that with Holoviews or Hvplot?
import hvplot.pandas
import pandas as pd
import numpy as np
df = pd.DataFrame(np.random.randn(20), columns=['Value'])
df['Source'] = ['Preds'] *10 +['Real'] * 10
df['Item'] = ['item1'] *5 + ['item2']*5 + ['item1'] *5 + ['item2']*5
df.hvplot.box(y='Value', by=['Item', 'Source'])
I would like the first graph of this image to be in the style of the second
You can do it by setting the color and cmap parameter:
df.hvplot.box(
y='Value',
by=['Item', 'Source'],
color='Source',
cmap=['blue', 'orange'],
legend=False,
)
Or by setting .opts(box_color):
df.hvplot.box(
y='Value',
by=['Item', 'Source'],
legend=False,
).opts(
box_color='Source',
cmap='Category20',
)
See also this SO question: Holoviews color per category
More info on choosing particular colors for plots:
http://holoviews.org/user_guide/Styling_Plots.html
http://holoviews.org/user_guide/Colormaps.html
Related
I have this mosaic:
from statsmodels.graphics.mosaicplot import mosaic
mosaic_data = pd.DataFrame({'gender': labeled_gender, 'y': labeled_y})
mosaic(mosaic_data, ['gender','y'], title='Mosaic of Heart Disease Vs. Gender', ax=ax, properties={})
I simply want to change the color palette to the colors / palette of my choice. Is there a way to do that? Also is there a way to reach & change other properties of the plot, for example color of the labels inside the rectangles?
To change the color you need to provide a mapping that matches the names. Don't think you can change the label color easily:
from statsmodels.graphics.mosaicplot import mosaic
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
fig, ax = plt.subplots(1, 1,figsize=(6,6))
gender = np.repeat(['male','female'],30)
heart_disease = [np.random.choice(['heart disease','no heart disease'],30,p=[0.8,0.2]),
np.random.choice(['heart disease','no heart disease'],30,p=[0.5,0.5])]
data = pd.DataFrame({'gender': gender, 'heart disease': np.array(heart_disease).flatten()})
cols = {('male', 'heart disease'):'#9a1f40',('male', 'no heart disease'):'#d9455f',
('female','heart disease' ):'#74d4c0', ('female', 'no heart disease'):'#def4f0'}
x = mosaic(data,['gender','heart disease'],
properties = lambda key: {'color': cols[key]} ,
ax=ax,gap=0.01)
I would like to plot certain slices of my Pandas Dataframe for each rows (based on row indexes) with different colors.
My data look like the following:
I already tried with the help of this tutorial to find a way but I couldn't - probably due to a lack of skills.
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
df = pd.read_csv("D:\SOF10.csv" , header=None)
df.head()
#Slice interested data
C = df.iloc[:, 2::3]
#Plot Temp base on row index colorfully
C.apply(lambda x: plt.scatter(x.index, x, c='g'))
plt.show()
Following is my expected plot:
I was also wondering if I could displace the mean of each row of the sliced data which contains 480 values somewhere in the plot or in the legend beside of plot! Is it feasible (like the following picture) to calculate the mean and displaced somewhere in the legend or by using small font size displace next to its own data in graph ?
Data sample: data
This gives the plot without legend
C = df.iloc[:,2::3].stack().reset_index()
C.columns = ['level_0', 'level_1', 'Temperature']
fig, ax = plt.subplots(1,1)
C.plot('level_0', 'Temperature',
ax=ax, kind='scatter',
c='level_0', colormap='tab20',
colorbar=False, legend=True)
ax.set_xlabel('Cycles')
plt.show()
Edit to reflect modified question:
stack() transform your (sliced) dataframe to a series with index (row, col)
reset_index() reset the double-level index above to level_0 (row), level_1 (col).
set_xlabel sets the label of x-axis to what you want.
Edit 2: The following produces scatter with legend:
CC = df.iloc[:,2::3]
fig, ax = plt.subplots(1,1, figsize=(16,9))
labels = CC.mean(axis=1)
for i in CC.index:
ax.scatter([i]*len(CC.columns[1:]), CC.iloc[i,1:], label=labels[i])
ax.legend()
ax.set_xlabel('Cycles')
ax.set_ylabel('Temperature')
plt.show()
This may be an approximate answer. scatter(c=, cmap= can be used for desired coloring.
import pandas as pd
import numpy as np
import matplotlib
import matplotlib.pyplot as plt
import matplotlib.cm as cm
import itertools
df = pd.DataFrame({'a':[34,22,1,34]})
fig, subplot_axes = plt.subplots(1, 1, figsize=(20, 10)) # width, height
colors = ['red','green','blue','purple']
cmap=matplotlib.colors.ListedColormap(colors)
for col in df.columns:
subplot_axes.scatter(df.index, df[col].values, c=df.index, cmap=cmap, alpha=.9)
In addition to the solution posted in this link I would also like if I can also add the Hue Parameter, and add the Median Values in each of the plots.
The Current Code:
testPlot = sns.boxplot(x='Pclass', y='Age', hue='Sex', data=trainData)
m1 = trainData.groupby(['Pclass', 'Sex'])['Age'].median().values
mL1 = [str(np.round(s, 2)) for s in m1]
p1 = range(len(m1))
for tick, label in zip(p1, testPlot.get_xticklabels()):
print(testPlot.text(p1[tick], m1[tick] + 1, mL1[tick]))
Gives a Output Like:
I'm working on the Titanic Dataset which can be found in this link.
I'm getting the required values, but only when I do a print statement, how do I include it in my Plot?
Place your labels manually according to hue parameter and width of bars for every category in a cycle of all xticklabels:
import seaborn as sns
import pandas as pd
import numpy as np
import matplotlib.pylab as plt
trainData = pd.read_csv('titanic.csv')
testPlot = sns.boxplot(x='pclass', y='age', hue='sex', data=trainData)
m1 = trainData.groupby(['pclass', 'sex'])['age'].median().values
mL1 = [str(np.round(s, 2)) for s in m1]
ind = 0
for tick in range(len(testPlot.get_xticklabels())):
testPlot.text(tick-.2, m1[ind+1]+1, mL1[ind+1], horizontalalignment='center', color='w', weight='semibold')
testPlot.text(tick+.2, m1[ind]+1, mL1[ind], horizontalalignment='center', color='w', weight='semibold')
ind += 2
plt.show()
This answer is nearly copy & pasted from here but fit more to your example code. The linked answer is IMHO a bit missplaced there because that question is just about labeling a boxplot and not about a boxplot using the hue argument.
I couldn't use your Train dataset because it is not available as Python package. So I used Titanic instead which has nearly the same column names.
#!/usr/bin/env python3
import pandas as pd
import matplotlib
import matplotlib.patheffects as path_effects
import seaborn as sns
def add_median_labels(ax, fmt='.1f'):
"""Credits: https://stackoverflow.com/a/63295846/4865723
"""
lines = ax.get_lines()
boxes = [c for c in ax.get_children() if type(c).__name__ == 'PathPatch']
lines_per_box = int(len(lines) / len(boxes))
for median in lines[4:len(lines):lines_per_box]:
x, y = (data.mean() for data in median.get_data())
# choose value depending on horizontal or vertical plot orientation
value = x if (median.get_xdata()[1] - median.get_xdata()[0]) == 0 else y
text = ax.text(x, y, f'{value:{fmt}}', ha='center', va='center',
fontweight='bold', color='white')
# create median-colored border around white text for contrast
text.set_path_effects([
path_effects.Stroke(linewidth=3, foreground=median.get_color()),
path_effects.Normal(),
])
df = sns.load_dataset('titanic')
plot = sns.boxplot(x='pclass', y='age', hue='sex', data=df)
add_median_labels(plot)
plot.figure.show()
Als an alternative when you create your boxplot with a figure-based function. In that case you need to give the axes parameter to add_median_labels().
# imports and add_median_labels() unchanged
df = sns.load_dataset('titanic')
plot = sns.catplot(kind='box', x='pclass', y='age', hue='sex', data=df)
add_median_labels(plot.axes[0][0])
plot.figure.show()
The resulting plot
This solution also works with more then two categories in the column used for the hue argument.
I need to change the colors of the boxplot drawn using pandas utility function. I can change most properties using the color argument but can't figure out how to change the facecolor of the box. Someone knows how to do it?
import pandas as pd
import numpy as np
data = np.random.randn(100, 4)
labels = list("ABCD")
df = pd.DataFrame(data, columns=labels)
props = dict(boxes="DarkGreen", whiskers="DarkOrange", medians="DarkBlue", caps="Gray")
df.plot.box(color=props)
While I still recommend seaborn and raw matplotlib over the plotting interface in pandas, it turns out that you can pass patch_artist=True as a kwarg to df.plot.box, which will pass it as a kwarg to df.plot, which will pass is as a kwarg to matplotlib.Axes.boxplot.
import pandas as pd
import numpy as np
data = np.random.randn(100, 4)
labels = list("ABCD")
df = pd.DataFrame(data, columns=labels)
props = dict(boxes="DarkGreen", whiskers="DarkOrange", medians="DarkBlue", caps="Gray")
df.plot.box(color=props, patch_artist=True)
As suggested, I ended up creating a function to plot this, using raw matplotlib.
def plot_boxplot(data, ax):
bp = ax.boxplot(data.values, patch_artist=True)
for box in bp['boxes']:
box.set(color='DarkGreen')
box.set(facecolor='DarkGreen')
for whisker in bp['whiskers']:
whisker.set(color="DarkOrange")
for cap in bp['caps']:
cap.set(color="Gray")
for median in bp['medians']:
median.set(color="white")
ax.axhline(0, color="DarkBlue", linestyle=":")
ax.set_xticklabels(data.columns)
I suggest using df.plot.box with patch_artist=True and return_type='both' (which returns the matplotlib axes the boxplot is drawn on and a dictionary whose values are the matplotlib Lines of the boxplot) in order to have the best customization possibilities.
For example, given this data:
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
df = pd.DataFrame(
data=np.random.randn(100, 4),
columns=list("ABCD")
)
you can set a specific color for all the boxes:
fig,ax = plt.subplots(figsize=(9,6))
ax,props = df.plot.box(patch_artist=True, return_type='both', ax=ax)
for patch in props['boxes']:
patch.set_facecolor('lime')
plt.show()
you can set a specific color for each box:
colors = ['green','blue','yellow','red']
fig,ax = plt.subplots(figsize=(9,6))
ax,props = df.plot.box(patch_artist=True, return_type='both', ax=ax)
for patch,color in zip(props['boxes'],colors):
patch.set_facecolor(color)
plt.show()
you can easily integrate a colormap:
colors = np.random.randint(0,10, 4)
cm = plt.cm.get_cmap('rainbow')
colors_cm = [cm((c-colors.min())/(colors.max()-colors.min())) for c in colors]
fig,ax = plt.subplots(figsize=(9,6))
ax,props = df.plot.box(patch_artist=True, return_type='both', ax=ax)
for patch,color in zip(props['boxes'],colors_cm):
patch.set_facecolor(color)
# to add colorbar
fig.colorbar(plt.cm.ScalarMappable(
plt.cm.colors.Normalize(min(colors),max(colors)),
cmap='rainbow'
), ax=ax, cmap='rainbow')
plt.show()
I've been trying to follow this How to make custom legend in matplotlib SO question but I think a few things are getting lost in translation. I used a custom color mapping for the different classes of points in my plot and I want to be able to put a table with those color-label pairs. I stored the info in a dictionary D_color_label and then made 2 parallel lists colors and labels. I tried using it in the ax.legend but it didn't seem to work.
np.random.seed(0)
# Create dataframe
DF_0 = pd.DataFrame(np.random.random((100,2)), columns=["x","y"])
# Label to colors
D_idx_color = {**dict(zip(range(0,25), ["#91FF61"]*25)),
**dict(zip(range(25,50), ["#BA61FF"]*25)),
**dict(zip(range(50,75), ["#916F61"]*25)),
**dict(zip(range(75,100), ["#BAF1FF"]*25))}
D_color_label = {"#91FF61":"label_0",
"#BA61FF":"label_1",
"#916F61":"label_2",
"#BAF1FF":"label_3"}
# Add color column
DF_0["color"] = pd.Series(list(D_idx_color.values()), index=list(D_idx_color.keys()))
# Plot
fig, ax = plt.subplots(figsize=(8,8))
sns.regplot(data=DF_0, x="x", y="y", scatter_kws={"c":DF_0["color"]}, ax=ax)
# Add custom legend
colors = list(set(DF_0["color"]))
labels = [D_color_label[x] for x in set(DF_0["color"])]
# If I do this, I get the following error:
# ax.legend(colors, labels)
# UserWarning: Legend does not support '#BA61FF' instances.
# A proxy artist may be used instead.
According to http://matplotlib.org/users/legend_guide.html you have to put to legend function artists which will be labeled. To use scatter_plot individually you have to group by your data by color and plot every data of one color individually to set its own label for every artist:
import pandas as pd
import numpy as np
import matplotlib.pylab as plt
import seaborn as sns
np.random.seed(0)
# Create dataframe
DF_0 = pd.DataFrame(np.random.random((100, 2)), columns=["x", "y"])
DF_0['color'] = ["#91FF61"]*25 + ["#BA61FF"]*25 + ["#91FF61"]*25 + ["#BA61FF"]*25
#print DF_0
D_color_label = {"#91FF61": "label_0", "#BA61FF": "label_1",
"#916F61": "label_2", "#BAF1FF": "label_3"}
colors = list(DF_0["color"].uniqe())
labels = [D_color_label[x] for x in DF_0["color"].unique()]
ax = sns.regplot(data=DF_0, x="x", y="y", scatter_kws={'c': DF_0['color'], 'zorder':1})
# Make a legend
# groupby and plot points of one color
for i, grp in DF_0.groupby(['color']):
grp.plot(kind='scatter', x='x', y='y', c=i, ax=ax, label=labels[i+1], zorder=0)
ax.legend(loc=2)
plt.show()