I'd like to plot an Histogram which makes comparisons between two arrays of data. Basically, i want to make exactly this:
Suppose i want to make this plot, but using two arrays with four entries, one with the numbers which should go to the blue areas, and the other with the ones for the blue areas. I have tried this:
x1 = np.array([0.1,0.2,0.3])
x2 = np.array([0.1,0.2,0.5])
sns.histplot(data=[x1,x2], x=['1','2','3'], multiple="dodge", hue=['a','b'], shrink=.8)
But it gives me the error “ValueError: arrays must all be same length”
I know that i'm supposed to enter a df and not arrays, but sadly i'm not really an expert on how to use them.
How can i solve this problem? Simply put, i'm looking for a copy and paste solution here, in which i can then change the numbers, and the name of the columns.
It looks like you want a barplot, not a histogram. Creating a seaborn plot from multiple columns usually involves converting them to "long form", making the process less straightforward.
Here is an example:
import matplotlib.pyplot as plt
import seaborn as sns
import pandas as pd
import numpy as np
x1 = np.array([0.1, 0.2, 0.3])
x2 = np.array([0.1, 0.2, 0.5])
x = ['1', '2', '3'] # or, simpler, x = np.arange(len(x1)) + 1
df = pd.DataFrame({'a': x1, 'b': x2, 'x': x})
df_long = df.melt('x')
ax = sns.barplot(data=df_long, x='x', y='value', dodge=True, hue='variable')
plt.show()
The long form looks like:
x variable value
0 1 a 0.1
1 2 a 0.2
2 3 a 0.3
3 1 b 0.1
4 2 b 0.2
5 3 b 0.5
See pandas' melt for additional options, such as naming the created columns.
I want to plot different values of x and y-axis from different CSVs into a simple plot.
csv1:
Time Buff
1 5
2 10
3 15
csv2:
Time1 Buff1
2 3
4 6
5 9
I have 5 different CSVs. I tried plotting to concatenate the dataframes into a single frame and plot it. But I was able to plot with only one x-axis:
df = pd.read_csv('csv1.txt)
df1 = pd.read_csv('csv2.txt)
join = pd.concat([df, df1], axis=1)
join.plot(x='Time', y=['Buff', 'Buff1'], kind='line')
join.plot(x='Time', y='Buff', x='Time1', y='Buff1') #doesn't work
I end up getting a plot with reference with only one x-axis (csv1). But how to plot both x and y column from the CSVs into the same plot?
you can plot two dataframes in the same axis if you specify that axis with ax=. Notice that I created the figure and axis using subplots before i plotted either of the dataframes.
import pandas as pd
import matplotlib.pyplot as plt
f,ax = plt.subplots()
df = pd.DataFrame({'Time':[1,2,3],'Buff':[5,4,3]})
df1 = pd.DataFrame({'Time1':[2,3,4],'Buff1':[5,7,8]})
df.plot(x='Time',y='Buff',ax=ax)
df1.plot(x='Time1',y='Buff1',ax=ax)
I have a few Pandas DataFrames sharing the same value scale, but having different columns and indices. When invoking df.plot(), I get separate plot images. what I really want is to have them all in the same plot as subplots, but I'm unfortunately failing to come up with a solution to how and would highly appreciate some help.
You can manually create the subplots with matplotlib, and then plot the dataframes on a specific subplot using the ax keyword. For example for 4 subplots (2x2):
import matplotlib.pyplot as plt
fig, axes = plt.subplots(nrows=2, ncols=2)
df1.plot(ax=axes[0,0])
df2.plot(ax=axes[0,1])
...
Here axes is an array which holds the different subplot axes, and you can access one just by indexing axes.
If you want a shared x-axis, then you can provide sharex=True to plt.subplots.
You can see e.gs. in the documentation demonstrating joris answer. Also from the documentation, you could also set subplots=True and layout=(,) within the pandas plot function:
df.plot(subplots=True, layout=(1,2))
You could also use fig.add_subplot() which takes subplot grid parameters such as 221, 222, 223, 224, etc. as described in the post here. Nice examples of plot on pandas data frame, including subplots, can be seen in this ipython notebook.
You can plot multiple subplots of multiple pandas data frames using matplotlib with a simple trick of making a list of all data frame. Then using the for loop for plotting subplots.
Working code:
import matplotlib.pyplot as plt
import pandas as pd
import numpy as np
# dataframe sample data
df1 = pd.DataFrame(np.random.rand(10,2)*100, columns=['A', 'B'])
df2 = pd.DataFrame(np.random.rand(10,2)*100, columns=['A', 'B'])
df3 = pd.DataFrame(np.random.rand(10,2)*100, columns=['A', 'B'])
df4 = pd.DataFrame(np.random.rand(10,2)*100, columns=['A', 'B'])
df5 = pd.DataFrame(np.random.rand(10,2)*100, columns=['A', 'B'])
df6 = pd.DataFrame(np.random.rand(10,2)*100, columns=['A', 'B'])
#define number of rows and columns for subplots
nrow=3
ncol=2
# make a list of all dataframes
df_list = [df1 ,df2, df3, df4, df5, df6]
fig, axes = plt.subplots(nrow, ncol)
# plot counter
count=0
for r in range(nrow):
for c in range(ncol):
df_list[count].plot(ax=axes[r,c])
count+=1
Using this code you can plot subplots in any configuration. You need to define the number of rows nrow and the number of columns ncol. Also, you need to make list of data frames df_list which you wanted to plot.
You can use the familiar Matplotlib style calling a figure and subplot, but you simply need to specify the current axis using plt.gca(). An example:
plt.figure(1)
plt.subplot(2,2,1)
df.A.plot() #no need to specify for first axis
plt.subplot(2,2,2)
df.B.plot(ax=plt.gca())
plt.subplot(2,2,3)
df.C.plot(ax=plt.gca())
etc...
You can use this:
fig = plt.figure()
ax = fig.add_subplot(221)
plt.plot(x,y)
ax = fig.add_subplot(222)
plt.plot(x,z)
...
plt.show()
You may not need to use Pandas at all. Here's a matplotlib plot of cat frequencies:
x = np.linspace(0, 2*np.pi, 400)
y = np.sin(x**2)
f, axes = plt.subplots(2, 1)
for c, i in enumerate(axes):
axes[c].plot(x, y)
axes[c].set_title('cats')
plt.tight_layout()
Option 1: Create subplots from a dictionary of dataframes with long (tidy) data
Assumptions:
There is a dictionary of multiple dataframes of tidy data that are either:
Created by reading in from files
Created by separating a single dataframe into multiple dataframes
The categories, cat, may be overlapping, but all dataframes don't necessarily contain all values of cat
hue='cat'
This example uses a dict of dataframes, but a list of dataframes would be similar.
If the dataframes are wide, use pandas.DataFrame.melt to convert them to long form.
Because dataframes are being iterated through, there's no guarantee that colors will be mapped the same for each plot
A custom color map needs to be created from the unique 'cat' values for all the dataframes
Since the colors will be the same, place one legend to the side of the plots, instead of a legend in every plot
Tested in python 3.10, pandas 1.4.3, matplotlib 3.5.1, seaborn 0.11.2
Imports and Test Data
import pandas as pd
import numpy as np # used for random data
import matplotlib.pyplot as plt
from matplotlib.patches import Patch # for custom legend - square patches
from matplotlib.lines import Line2D # for custom legend - round markers
import seaborn as sns
import math import ceil # determine correct number of subplot
# synthetic data
df_dict = dict()
for i in range(1, 7):
np.random.seed(i) # for repeatable sample data
data_length = 100
data = {'cat': np.random.choice(['A', 'B', 'C'], size=data_length),
'x': np.random.rand(data_length), 'y': np.random.rand(data_length)}
df_dict[i] = pd.DataFrame(data)
# display(df_dict[1].head())
cat x y
0 B 0.944595 0.606329
1 A 0.586555 0.568851
2 A 0.903402 0.317362
3 B 0.137475 0.988616
4 B 0.139276 0.579745
# display(df_dict[6].tail())
cat x y
95 B 0.881222 0.263168
96 A 0.193668 0.636758
97 A 0.824001 0.638832
98 C 0.323998 0.505060
99 C 0.693124 0.737582
Create color mappings and plot
# create color mapping based on all unique values of cat
unique_cat = {cat for v in df_dict.values() for cat in v.cat.unique()} # get unique cats
colors = sns.color_palette('tab10', n_colors=len(unique_cat)) # get a number of colors
cmap = dict(zip(unique_cat, colors)) # zip values to colors
col_nums = 3 # how many plots per row
row_nums = math.ceil(len(df_dict) / col_nums) # how many rows of plots
# create the figue and axes
fig, axes = plt.subplots(row_nums, col_nums, figsize=(9, 6), sharex=True, sharey=True)
# convert to 1D array for easy iteration
axes = axes.flat
# iterate through dictionary and plot
for ax, (k, v) in zip(axes, df_dict.items()):
sns.scatterplot(data=v, x='x', y='y', hue='cat', palette=cmap, ax=ax)
sns.despine(top=True, right=True)
ax.legend_.remove() # remove the individual plot legends
ax.set_title(f'dataset = {k}', fontsize=11)
fig.tight_layout()
# create legend from cmap
# patches = [Patch(color=v, label=k) for k, v in cmap.items()] # square patches
patches = [Line2D([0], [0], marker='o', color='w', markerfacecolor=v, label=k, markersize=8) for k, v in cmap.items()] # round markers
# place legend outside of plot; change the right bbox value to move the legend up or down
plt.legend(title='cat', handles=patches, bbox_to_anchor=(1.06, 1.2), loc='center left', borderaxespad=0, frameon=False)
plt.show()
Option 2: Create subplots from a single dataframe with multiple separate datasets
The dataframes must be in a long form with the same column names.
This option uses pd.concat to combine multiple dataframes into a single dataframe, and .assign to add a new column.
See Import multiple csv files into pandas and concatenate into one DataFrame for creating a single dataframes from a list of files.
This option is easier because it doesn't require manually mapping colors to 'cat'
Combine DataFrames
# using df_dict, with dataframes as values, from the top
# combine all the dataframes in df_dict to a single dataframe with an identifier column
df = pd.concat((v.assign(dataset=k) for k, v in df_dict.items()), ignore_index=True)
# display(df.head())
cat x y dataset
0 B 0.944595 0.606329 1
1 A 0.586555 0.568851 1
2 A 0.903402 0.317362 1
3 B 0.137475 0.988616 1
4 B 0.139276 0.579745 1
# display(df.tail())
cat x y dataset
595 B 0.881222 0.263168 6
596 A 0.193668 0.636758 6
597 A 0.824001 0.638832 6
598 C 0.323998 0.505060 6
599 C 0.693124 0.737582 6
Plot a FacetGrid with seaborn.relplot
sns.relplot(kind='scatter', data=df, x='x', y='y', hue='cat', col='dataset', col_wrap=3, height=3)
Both options create the same result, however, it's less complicated to combine all the dataframes, and plot a figure-level plot with sns.relplot.
Building on #joris response above, if you have already established a reference to the subplot, you can use the reference as well. For example,
ax1 = plt.subplot2grid((50,100), (0, 0), colspan=20, rowspan=10)
...
df.plot.barh(ax=ax1, stacked=True)
Here is a working pandas subplot example, where modes is the column names of the dataframe.
dpi=200
figure_size=(20, 10)
fig, ax = plt.subplots(len(modes), 1, sharex="all", sharey="all", dpi=dpi)
for i in range(len(modes)):
ax[i] = pivot_df.loc[:, modes[i]].plot.bar(figsize=(figure_size[0], figure_size[1]*len(modes)),
ax=ax[i], title=modes[i], color=my_colors[i])
ax[i].legend()
fig.suptitle(name)
import numpy as np
import pandas as pd
imoprt matplotlib.pyplot as plt
fig, ax = plt.subplots(2,2)
df = pd.DataFrame({'A':np.random.randint(1,100,10),
'B': np.random.randint(100,1000,10),
'C':np.random.randint(100,200,10)})
for ax in ax.flatten():
df.plot(ax =ax)
I need to draw many such rows (for a0 .. a128) in a single window. I've searched in FacetGrid, PairGrid and all over around but couldn't find. Only regplot has similar argument ax but it doesn't plot histograms. My data is 128 real valued features with label column [0, 1]. I need the graphs to be shown from my Python code as a separate application on Linux.
Also, it there a way to scale this histogram to show relative values on Y such that the right curve is not skewed?
g = sns.FacetGrid(df, col="Result")
g.map(plt.hist, "a0", bins=20)
plt.show()
Just a simple example using matplotlib. The code is not optimized (ugly, but simple plot-indexing):
import numpy as np
import matplotlib.pyplot as plt
N = 5
data = np.random.normal(size=(N*N, 1000))
f, axarr = plt.subplots(N, N) # maybe you want sharex=True, sharey=True
pi = [0,0]
for i in range(data.shape[0]):
if pi[1] == N:
pi[0] += 1 # next row
pi[1] = 0 # first column again
axarr[pi[0], pi[1]].hist(data[i], normed=True) # i was wrong with density;
# normed=True should be used
pi[1] += 1
plt.show()
Output:
I have clustered 3 features Feature1, Feature2 and Feature3 and came up with 2 clusters.
I am trying to visualize a 3D cluster using matplotlib.
In the below table, there are three features upon which the clustering is executed. Number of clusters is 2.
Feature1 Feature2 Feature3 ClusterIndex
0 1.349656e-09 1.000000 1.090542e-09 0
1 1.029752e-07 1.000000 6.040669e-08 0
2 2.311729e-07 1.000000 1.568289e-11 0
3 1.455860e-08 6.05e-08 1.000000 1
4 3.095807e-07 2.07e-07 1.000000 1
Tried this code:
fig = plt.figure()
ax = fig.add_subplot(111, projection='3d')
x = np.array(df['Feature1'])
y = np.array(df['Feature2'])
z = np.array(df['Feature3'])
ax.scatter(x,y,z, marker=colormap[kmeans.labels_], s=40)
However, I get the error "ValueError: could not convert string to float: red". The marker part is thus where I get the error.
2D visualization of clusters is pretty simple by plotting the points in a scatter plot and distinguishing it with cluster labels.
Just wondering is there a way to do 3D visualization of clusters.
Any suggestions would be highly appreciated !!
In principle, the code from the question should work. However it is unclear what marker=colormap[kmeans.labels_] would do and why it is needed.
The 3D scatter plot works exactly as the 2D version of it.
The marker argument would expect a marker string, like "s" or "o" to determine the marker shape.
The color can be set using the c argument. You can provide a single color or an array/a list of colors. In the example below we simply provide the cluster index to c and use a colormap.
import matplotlib.pyplot as plt
from mpl_toolkits.mplot3d import Axes3D
import pandas as pd
import numpy as np
v = np.random.rand(10,4)
v[:,3] = np.random.randint(0,2,size=10)
df = pd.DataFrame(v, columns=['Feature1', 'Feature2','Feature3',"Cluster"])
print (df)
fig = plt.figure()
ax = fig.add_subplot(111, projection='3d')
x = np.array(df['Feature1'])
y = np.array(df['Feature2'])
z = np.array(df['Feature3'])
ax.scatter(x,y,z, marker="s", c=df["Cluster"], s=40, cmap="RdBu")
plt.show()