I am trying to plot a scatter chart with pandas and matplotlib.pylot. The dots in the graph are only using one color, while the legend is showing there are three different colors for three different groups of data.
Below is my code and a copy of screen shot. You can see that only all dots are in green color. Could anyone point me why? What did I do wrong?
Thanks a lot in advance.
import pandas as pd
import matplotlib.pyplot as plt
data = {
'x':[1,2,3,4,1,3,7,5],
'y':[10, 20, 30, 40, 20, 30, 40, 80],
'label':['A', 'A','B','B','A','C','C','A']
}
df = pd.DataFrame(data)
plt.figure(figsize=(34,8))
fig,ax = plt.subplots()
#sns.scatterplot(data=df, hue='label', x='x', y='y')
for k, d in df.groupby('label'):
ax.scatter(df['x'], df['y'], label=k)
plt.legend()
plt.show()
You need to add colors mapping. Slight modifications to your code after adding colors dictionary:
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import matplotlib.cm as cm
data = {
'x':[1,2,3,4,1,3,7,5],
'y':[10, 20, 30, 40, 20, 30, 40, 80],
'label':['A', 'A','B','B','A','C','C','A']
}
df = pd.DataFrame(data)
#plt.figure(figsize=(34,8))
fig,ax = plt.subplots()
df1 = df.groupby('label')
colors = iter(cm.rainbow(np.linspace(0, 1, len(df1.groups))))
for k, d in df1:
ax.scatter(d['x'], d['y'], label=k, color=next(colors))
plt.legend()
plt.show()
outputs the scatter plot as:
Is this your desired output?
Related
I have a data frame with four columns I would like to plot the normality test for each column in a 2*2 grid, but it only plot one figure, and the else is empty.
import random
import pandas as pd
import numpy as np
from scipy import stats
import matplotlib.pyplot as plt
fig, axs = plt.subplots(2,2, figsize=(15, 6), facecolor='w', edgecolor='k')
fig.subplots_adjust(hspace = .5, wspace=.001)
data = {'col1': [random.randrange(1, 50, 1) for i in range(1000)], 'col2': [random.randrange(1, 50, 1) for i in range(1000)],'col3':[random.randrange(1, 50, 1) for i in range(1000)]
,'col4':[random.randrange(1, 50, 1) for i in range(1000)]}
df = pd.DataFrame(data)
for ax, d in zip(axs.ravel(), df):
ax=stats.probplot(df[d], plot=plt)
#ax.set_title(str(d))
plt.show()
is there a way to construct the subplot and the stats.probplot within a loop?
In your code, you need to change the for loop to this:
for ax, d in zip(axs.ravel(), df):
stats.probplot(df[d], plot=ax)
#ax.set_titl(str(d))
plt.show()
I hope this will help you move on.
Question: Using a scatter plot in matplotlib, is there a simple way get a half-filled marker?
I know half-filled markers can easily be done using a line plot, but I would like to use 'scatter' because I want to use marker size and color (i.e., alternate marker face color) to represent other data. (I believe this will be easier with a scatter plot since I want to automate making a large number of plots from a large data set.)
I can't seem to make half-filled markers properly using a scatter plot. That is to say, instead of a half-filled marker, the plot shows half of a marker. I've been using matplotlib.markers.MarkerStyle, but that seems to only get me halfway there. I'm able to get following output using the code below.
import numpy as np
import matplotlib.pyplot as plt
from matplotlib.markers import MarkerStyle
plt.scatter(1, 1, marker=MarkerStyle('o', fillstyle='full'), edgecolors='k', s=500)
plt.scatter(2, 2, marker=MarkerStyle('o', fillstyle='left'), edgecolors='k', s=500)
plt.scatter(3, 3, marker=MarkerStyle('o', fillstyle='right'), edgecolors='k', s=500)
plt.scatter(4, 4, marker=MarkerStyle('o', fillstyle='top'), edgecolors='k', s=500)
plt.scatter(5, 5, marker=MarkerStyle('o', fillstyle='bottom'), edgecolors='k', s=500)
plt.show()
As mentioned in the comments, I don't see why you have to use plt.scatter but if you want to, you can fake a combined marker:
from matplotlib.markers import MarkerStyle
from matplotlib import pyplot as plt
#data generation
import pandas as pd
import numpy as np
np.random.seed(123)
n = 10
df = pd.DataFrame({"X": np.random.randint(1, 20, n),
"Y": np.random.randint(10, 30, n),
"S": np.random.randint(50, 500, n),
"C1": np.random.choice(["red", "blue", "green"], n),
"C2": np.random.choice(["yellow", "grey"], n)})
fig, ax = plt.subplots()
ax.scatter(df.X, df.Y, s=df.S, c=df.C1, edgecolor="black", marker=MarkerStyle("o", fillstyle="right"))
ax.scatter(df.X, df.Y, s=df.S, c=df.C2, edgecolor="black", marker=MarkerStyle("o", fillstyle="left"))
plt.show()
Sample output:
This works, of course, also for continuous data:
from matplotlib import pyplot as plt
from matplotlib.markers import MarkerStyle
import pandas as pd
import numpy as np
np.random.seed(123)
n = 10
df = pd.DataFrame({"X": np.random.randint(1, 20, n),
"Y": np.random.randint(10, 30, n),
"S": np.random.randint(100, 1000, n),
"C1": np.random.randint(1, 100, n),
"C2": np.random.random(n)})
fig, ax = plt.subplots(figsize=(10,8))
im1 = ax.scatter(df.X, df.Y, s=df.S, c=df.C1, edgecolor="black", marker=MarkerStyle("o", fillstyle="right"), cmap="autumn")
im2 = ax.scatter(df.X, df.Y, s=df.S, c=df.C2, edgecolor="black", marker=MarkerStyle("o", fillstyle="left"), cmap="winter")
cbar1 = plt.colorbar(im1, ax=ax)
cbar1.set_label("right half", rotation=90)
cbar2 = plt.colorbar(im2, ax=ax)
cbar2.set_label("left half", rotation=90)
plt.show()
Sample output:
But be reminded that plt.plot with marker definitions might be faster for large-scale datasets: The plot function will be faster for scatterplots where markers don't vary in size or color.
I am trying to plot an array of 101 rows * 12 Columns, with row #1 as a highlight using the code below:
plt.plot(HW.transpose()[1:101],color = 'grey', alpha = 0.1)
plt.plot(HW.transpose()[0],color = 'red', linewidth = 3, alpha = 0.7)
The only issue in this graph is that 'S1' somehow ends up in the last instead of beginning. What am I doing wrong?
HW.transpose()[1:101] doesn't select the desired columns. You can use HW.transpose().iloc[:, 1:101] instead:
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
HW = pd.DataFrame(np.random.randn(101, 12).cumsum(axis=1), columns=[f'S{i}' for i in range(1, 13)])
plt.plot(HW.transpose().iloc[:, 1:101], color='grey', alpha=0.1)
plt.plot(HW.transpose().iloc[:, 0], color='red', linewidth=3, alpha=0.7)
plt.show()
I would like to remove legends from individual subplots in panda. I created a bar chart and the #subplots. I would like to keep the titles of each subplot and remove the legends since they show the #same verbiage. I have tried several techniques, and even some that has me calling on each individual #subplot but am sure there is a simple solution. The fourth result image below is the one I need help with.
Here is my code so far:
import matplotlib.pyplot as plt
import pandas as pd
import scipy.stats as st
import numpy as np
mouse_metadata = "Mouse_metadata.csv"
study_results = "Study_results.csv"
mouse_metadata = pd.read_csv(mouse_metadata)
study_results = pd.read_csv(study_results)
study_data_combined = pd.merge(mouse_metadata,study_results, on= "Mouse ID")
pyma_sd = study_data_combined
pyma_sd.head()
pyma_sd_grouped = pyma_sd.groupby(["Drug Regimen"])
pyma_sd_grouped_mean = pyma_sd_grouped["Tumor Volume (mm3)"].mean()
pyma_sd_grouped_median = pyma_sd_grouped["Tumor Volume (mm3)"].median()
pyma_sd_grouped_variance = pyma_sd_grouped["Tumor Volume (mm3)"].var()
pyma_sd_grouped_std = pyma_sd_grouped["Tumor Volume (mm3)"].std()
pyma_sd_grouped_sem = pyma_sd_grouped["Tumor Volume (mm3)"].sem()
pyma_sd_grouped_stats = pd.DataFrame({ "Mean":pyma_sd_grouped_mean,
"Median": pyma_sd_grouped_median,
"Variance": pyma_sd_grouped_variance,
"Standard Error of Mean ": pyma_sd_grouped_sem})
print(" ","Stats of Tumor Volume")
print(pyma_sd_grouped_stats)
chart_pyma_sd_grouped_stats = pyma_sd_grouped_stats.plot(kind='bar', rot=50, figsize = (10, 6),
width = .8)
plt.title("Stats on Drug Regimen")Output 2
plt.xlabel("Drug Regimen")
plt.ylabel("Stats per Drug Regimen")
plt.tight_layout()
plt.show()
axes = pyma_sd_grouped_stats.plot.bar(rot=50, subplots=True, figsize = (10, 6), width = .75,)
axes[1].legend(loc=1)
plt.subplots_adjust(hspace=0.5)
plt.show()
**
Simply supply legend=False in your call to DataFrame.plot.bar.
import matplotlib.pyplot as plt
import pandas as pd
speed = [0.1, 17.5, 40, 48, 52, 69, 88]
lifespan = [2, 8, 70, 1.5, 25, 12, 28]
index = ['snail', 'pig', 'elephant', 'rabbit', 'giraffe', 'coyote', 'horse']
df = pd.DataFrame({'speed': speed, 'lifespan': lifespan}, index=index)
axes = df.plot.bar(rot=0, subplots=True, legend=False)
plt.show()
Compare the image above to the one generated in the doc.
As an exercise, I'm reproducing a plot from The Economist with matplotlib
So far, I can generate a random data and produce two plots independently. I'm struggling now with putting them next to each other horizontally.
import pandas as pd
import matplotlib.pyplot as plt
import numpy as np
%matplotlib inline
df1 = pd.DataFrame({"broadcast": np.random.randint(110, 150,size=8),
"cable": np.random.randint(100, 250, size=8),
"streaming" : np.random.randint(10, 50, size=8)},
index=pd.Series(np.arange(2009,2017),name='year'))
df1.plot.bar(stacked=True)
df2 = pd.DataFrame({'usage': np.sort(np.random.randint(1,50,size=7)),
'avg_hour': np.sort(np.random.randint(0,3, size=7) + np.random.ranf(size=7))},
index=pd.Series(np.arange(2009,2016),name='year'))
plt.figure()
fig, ax1 = plt.subplots()
ax1.plot(df2['avg_hour'])
ax2 = ax1.twinx()
ax2.bar(left=range(2009,2016),height=df2['usage'])
plt.show()
You should try using subplots. First you create a figure by plt.figure(). Then add one subplot(121) where 1 is number of rows, 2 is number of columns and last 1 is your first plot. Then you plot the first dataframe, note that you should use the created axis ax1. Then add the second subplot(122) and repeat for the second dataframe. I changed your axis ax2 to ax3 since now you have three axis on one figure. The code below produces what I believe you are looking for. You can then work on aesthetics of each plot separately.
%matplotlib inline
import pandas as pd
import matplotlib.pyplot as plt
import numpy as np
fig = plt.figure()
df1 = pd.DataFrame({"broadcast": np.random.randint(110, 150,size=8),
"cable": np.random.randint(100, 250, size=8),
"streaming" : np.random.randint(10, 50, size=8)},
index=pd.Series(np.arange(2009,2017),name='year'))
ax1 = fig.add_subplot(121)
df1.plot.bar(stacked=True,ax=ax1)
df2 = pd.DataFrame({'usage': np.sort(np.random.randint(1,50,size=7)),
'avg_hour': np.sort(np.random.randint(0,3, size=7) + np.random.ranf(size=7))},
index=pd.Series(np.arange(2009,2016),name='year'))
ax2 = fig.add_subplot(122)
ax2.plot(df2['avg_hour'])
ax3 = ax2.twinx()
ax3.bar(left=range(2009,2016),height=df2['usage'])
plt.show()