I want to change the scale of the sns.kdeplot cbar, so I can see the number of points instead of a decimal number (honestly I don't fully understand it).
The code:
import numpy as np
import pandas as pd
df = pd.DataFrame(np.random.randint(0,50,size=(50,2 )), columns=list('AB'))
sns.kdeplot(df['A'], df['B'],cmap='Reds',shade=True,shade_lowest=False,cbar=True)
The result:
Related
I have 2 tables a 10 by 110 and a 35 by 110 and both contain random numbers from a exponential distribution function provided by my professor. The assignment is to prove the central limit theorem in statistics.
What I thought to try is:
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
"importing data"
df1 = pd.read_excel(r'C:\Users\Henry\Desktop\n10.xlsx')
df2 = pd.read_excel(r'C:\Users\Henry\Desktop\n30.xlsx')
df1avg = pd.read_excel(r'C:\Users\Henry\Desktop\n10avg.xlsx')
df2avg = pd.read_excel(r'C:\Users\Henry\Desktop\n30avg.xlsx')
"plotting n10 histogram"
plt.hist(df1, bins=34)
plt.hist(df1avg, bins=11)
"plotting n30 histogram"
plt.hist(df2, bins=63)
plt.hist(df2avg, bins=11)
Is that ok or do I need to format the tables into a singular column, and if so what is the most efficient way to do that?
I suspect that you will want to flatten your dataframe first, as illustrated below.
import numpy as np
import matplotlib.pyplot as plt
import pandas as pd
N = np.random.exponential(1, [40, 5])
df = pd.DataFrame(N) # convert to dataframe
bin_edges = np.linspace(0,6,30)
plt.figure()
plt.hist(df, bins = bin_edges, density = True)
plt.xlabel('Value')
plt.ylabel('Probability density')
The multiple (5) colours of lines per bin shows the histograms for each column of the data frame.
Fortunately, this is not hard to adjust. You can convert the data frame to a numpy array and flatten it:
plt.hist(df.to_numpy().flatten(), bins = bin_edges, density = True)
plt.ylabel('Probability density')
plt.xlabel('Value')
I can plot multiple histograms in a single plot using pandas but there are few things missing:
How to give the label.
I can only plot one figure, how to change it to layout=(3,1) or something else.
Also, in figure 1, all the bins are filled with solid colors, and its kind of difficult to know which is which, how to fill then with different markers (eg. crosses,slashes,etc)?
Here is the MWE:
import numpy as np
import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt
df = sns.load_dataset('iris')
df.groupby('species')['sepal_length'].hist(alpha=0.7,label='species')
plt.legend()
Output:
To change layout I can use by keyword, but can't give them colors
HOW TO GIVE DIFFERENT COLORS?
df.hist('sepal_length',by='species',layout=(3,1))
plt.tight_layout()
Gives:
You can resolve to groupby:
fig,ax = plt.subplots()
hatches = ('\\', '//', '..') # fill pattern
for (i, d),hatch in zip(df.groupby('species'), hatches):
d['sepal_length'].hist(alpha=0.7, ax=ax, label=i, hatch=hatch)
ax.legend()
Output:
In pandas version 1.1.0 you can simply set the legend keyword to true.
import numpy as np
import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt
df = sns.load_dataset('iris')
df.groupby('species')['sepal_length'].hist(alpha=0.7, legend = True)
output image
It's more code, but using pure matplotlib will always give you more control over the plots. For your second case:
import matplotlib.pyplot as plt
import numpy as np
from itertools import zip_longest
# Dictionary of color for each species
color_d = dict(zip_longest(df.species.unique(),
plt.rcParams['axes.prop_cycle'].by_key()['color']))
# Use the same bins for each
xmin = df.sepal_length.min()
xmax = df.sepal_length.max()
bins = np.linspace(xmin, xmax, 20)
# Set up correct number of subplots, space them out.
fig, ax = plt.subplots(nrows=df.species.nunique(), figsize=(4,8))
plt.subplots_adjust(hspace=0.4)
for i, (lab, gp) in enumerate(df.groupby('species')):
ax[i].hist(gp.sepal_length, ec='k', bins=bins, color=color_d[lab])
ax[i].set_title(lab)
# same xlim for each so we can see differences
ax[i].set_xlim(xmin, xmax)
I am trying to have different point sizes on a seaboard scatterplot depending on the value on the "hue" column of my dataframe.
sns.scatterplot(x="X", y="Y", data=df, hue='value',style='value')
value can take 3 different values (0,1 and 2) and I would like points which value is 2 to be bigger on the graph.
I tried the sizes argument :
sizes=(1,1,4)
But could not get it done this way.
Let's use the s parameter and pass a list of sizes using a function of df['value'] to scale the point sizes:
df = pd.DataFrame({'X':[1,2,3],'Y':[1,4,9],'value':[1,0,2]})
import seaborn as sns
_ = sns.scatterplot(x='X',y='Y', data=df, s=df['value']*50+10)
Output:
Using seaborn scatterplots arguments:
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
df = pd.DataFrame({'X':[1,2,3,4,5],'Y':[1,2,3,4,5],
'value':[1,1,0,2,2]})
df["size"] = np.where(df["value"] == 2, "Big", "Small")
sns.scatterplot(x="X", y="Y", hue='value', size="size",
data=df, size_order=("Small", "Big"), sizes=(160, 40))
plt.show()
Note that the order of sizes needs to be reveresed compared to the size_order. I have no idea why that would make sense, though.
I folks,
Consider the following example
import matplotlib.pyplot as plt
import pandas as pd
import numpy as np
fig, (ax1,ax2) = plt.subplots(2,1)
dates = pd.date_range("2018-01-01","2019-01-01",freq = "1d")
x = pd.DataFrame(index = dates, data = np.linspace(0,1,len(dates)) )
x.plot(ax=ax1)
y = np.random.random([len(dates),100]) * x.values
ax2.pcolormesh(range(len(x)), np.linspace(-1,1,100), y.T)
plt.show()
At this point, I would like the both axis (ax1,ax2) to share the x-axis, i.e. displaying proper pandas dates on the second axis. sharex=True does not seem to work. How can I achieve that? I tried different possibilities which did not work out.
Edit: Since the pandas date formatting is superior to the native matplotlib formatting, please provide me with a solution where pandas date formatting is used (for instance, zooming with an interactive environment works much better with pandas date formatting). Thanks You!
One way to do it would be to do all the plotting with matplotlib, this way there are no problems with the different time formats being used:
import matplotlib.pyplot as plt
import pandas as pd
import numpy as np
fig, (ax1,ax2) = plt.subplots(2,1, sharex='col')
dates = pd.date_range("2018-01-01","2019-01-01",freq = "1d")
x = pd.DataFrame(index = dates, data = np.linspace(0,1,len(dates)) )
#x.plot(ax=ax1)
ax1.plot(x.index, x.values)
y = np.random.random([len(dates),100]) * x.values
ax2.pcolormesh(x.index, np.linspace(-1,1,100), y.T)
fig.tight_layout()
plt.show()
This gives the following plot:
What seems to work fine is to first plot the same line into the axes that should host the image, then plot the image, then remove the line again. What this does is that it tells pandas to apply its locators and formatters to that axes; they will stay after removing the line.
import matplotlib.pyplot as plt
import pandas as pd
import numpy as np
fig, (ax1,ax2) = plt.subplots(2,1, sharex=True)
dates = pd.date_range("2018-01-01","2019-01-01",freq = "1d")
x = pd.DataFrame(index = dates, data = np.linspace(0,1,len(dates)) )
x.plot(ax=ax1)
y = np.random.random([len(dates),100]) * x.values
x.plot(ax=ax2, legend=False)
ax2.pcolormesh(dates, np.linspace(-1,1,100), y.T)
ax2.lines[0].remove()
plt.show()
Note that there may be caveats of this solution when zooming or panning. Consider it more like a hack and use it as long as it works, but don't blame anyone once it doesn't.
I'm currently using df.hist(alpha = .5), but all of the subplots are too close from each other, like this:
Histograms
Which way is better to change the space between them?
Or is better to plot each one in a separate .png file?
One simple way is to manipulate figsize and add pyplot.tight_layout. Below is the example.
Without adjustment:
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
df = pd.DataFrame(np.random.randn(6400)
.reshape((100, 64)), columns=['col_{}'.format(i) for i in range(64)])
df.hist(alpha=0.5)
plt.show()
You will get this as you showed:
In contrast, if you add figsize (with arbitrary size) and pyplot.tight_layout like below:
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
df = pd.DataFrame(np.random.randn(6400)
.reshape((100, 64)), columns=['col_{}'.format(i) for i in range(64)])
df.hist(alpha=0.5, figsize=(20, 10))
plt.tight_layout()
plt.show()
In this case you will get more aligned view:
Hope this helps.