I'm trying to plot a graph grouped by column values using a for loop without knowing the number of unique values in that column.
You can see sample code below (without a for loop) and the desired output.
I would like that each plot will have different color and marker (as seen below).
This is the code:
import pandas as pd
from numpy import random
df = pd.DataFrame(data = random.randn(5,4), index = ['A','B','C','D','E'],
columns = ['W','X','Y','Z'])
df['W'] = ['10/01/2018 12:00:00','10/03/2018 13:00:00',
'10/03/2018 12:30:00','10/04/2018 12:05:00',
'10/08/2018 12:00:15']
df['W']=pd.to_datetime(df['W'])
df['Entity'] = ['C201','C201','C201','C202','C202']
print(df.head())
fig, ax = plt.subplots()
df[df['Entity']=="C201"].plot(x="W",y="Y",label='C201',ax=ax,marker='x')
df[df['Entity']=="C202"].plot(x="W",y="Y",label='C202',ax=ax, marker='o')
This is the output:
You can first find out the unique values of your df['Entity'] and then loop over them. To generate new markers automatically for each Entity, you can define an order of some markers (let's say 5 in the answer below) which will repeat via marker=next(marker).
Complete minimal answer
import itertools
import pandas as pd
from numpy import random
import matplotlib.pyplot as plt
marker = itertools.cycle(('+', 'o', '*', '^', 's'))
df = pd.DataFrame(data = random.randn(5,4), index = ['A','B','C','D','E'],
columns = ['W','X','Y','Z'])
df['W'] = ['10/01/2018 12:00:00','10/03/2018 13:00:00',
'10/03/2018 12:30:00','10/04/2018 12:05:00',
'10/08/2018 12:00:15']
df['W']=pd.to_datetime(df['W'])
df['Entity'] = ['C201','C201','C201','C202','C202']
fig, ax = plt.subplots()
for idy in np.unique(df['Entity'].values):
df[df['Entity']==idy].plot(x="W",y="Y", label=idy, ax=ax, marker=next(marker))
plt.legend()
plt.show()
Related
I'd like to show the occurrence in a color map for the frequency of a point , i.e. (1,2) has a frequency of 3 points while still keeping my 'xaxis' (i.e. df['A'])
import pandas as pd
import matplotlib.pyplot as plt
df = pd.DataFrame({'A': [1,1,1,1,2,2,3,4,6,7,7],
'B': [2,2,2,3,3,4,5,6,7,8,8]})
plt.figure()
plt.scatter(df['A'], df['B'])
plt.show()
Here is my current plot
I'd like to keep the same axis I have, while adding the colormap. Hope I was being clear.
You can calculate the frequency of a certain value using the collections package.
freq_dic = collections.Counter(df["B"])
You then need to add this new list to your dataframe and add two new options to the scatter plot. The colormap legend is displayed with plt.colorbar. This code is far from perfect, so any further improvements are very welcome.
import pandas as pd
import matplotlib.pyplot as plt
import collections
df = pd.DataFrame({'A': [1,1,1,1,2,2,3,4,6,7,7],
'B': [2,2,2,3,3,4,5,6,7,8,8]})
freq_dic = collections.Counter(df["B"])
for index, entry in enumerate(df["B"]):
df.at[index, 'freq'] = (freq_dic[entry])
plt.figure()
plt.scatter(df['A'], df['B'],
c=df['freq'],
cmap='viridis')
plt.colorbar()
plt.show()
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)
Hello,
I'm trying to plot a box plot combining columns from two different data frames. Help please :)
This is the code:
import pandas as pd
from numpy import random
#Generating the data frame
df1 = pd.DataFrame(data = random.randn(5,2), columns = ['W','Y'])
df2 = pd.DataFrame(data = random.randn(5,2), columns = ['X','Y'])
print(df1.head())
print('\n')
print(df2.head())
This is the output:
This is what I want to get:
The following will give you what you desire:
import matplotlib.pyplot as plt
fig, ax = plt.subplots(1, 1)
ax.boxplot([df1['Y'], df2['Y']], positions=[1, 2])
ax.set_xticklabels(['W', 'X'])
ax.set_ylabel('Y')
This gave me the plot below (which I think is what you were aiming for):
I'm trying to create a scatterplot of a dataset with point coloring based on different categorical columns. Seaborn works well here for one plot:
fg = sns.FacetGrid(data=plot_data, hue='col_1')
fg.map(plt.scatter, 'x_data', 'y_data', **kws).add_legend()
plt.show()
I then want to display the same data, but with hue='col_2' and hue='col_3'. It works fine if I just make 3 plots, but I'm really hoping to find a way to have them all appear as subplots in one figure. Unfortunately, I haven't found any way to change the hue from one plot to the next. I know there are plotting APIs that allow for an axis keyword, thereby letting you pop it into a matplotlib figure, but I haven't found one that simultaneously allows you to set 'ax=' and 'hue='. Any ideas?
Thanks in advance!
Edit:
Here's some sample code to illustrate the idea
xx = np.random.rand(10,2)
cat1 = np.array(['cat','dog','dog','dog','cat','hamster','cat','cat','hamster','dog'])
cat2 = np.array(['blond','brown','brown','black','black','blond','blond','blond','brown','blond'])
d = {'x':xx[:,0], 'y':xx[:,1], 'pet':cat1, 'hair':cat2}
df = pd.DataFrame(data=d)
sns.set(style='ticks')
fg = sns.FacetGrid(data=df, hue='pet', size=5)
fg.map(plt.scatter, 'x', 'y').add_legend()
fg = sns.FacetGrid(data=df, hue='hair', size=5)
fg.map(plt.scatter, 'x', 'y').add_legend()
plt.show()
This plots what I want, but in two windows. The color scheme is set in the first plot by grouping by 'pet', and in the second plot by 'hair'. Is there any way to do this on one plot?
In order to plot 3 scatterplots with different colors for each, you may create 3 axes in matplotlib and plot a scatter to each axes.
import pandas as pd
import numpy as np; np.random.seed(42)
import matplotlib.pyplot as plt
df = pd.DataFrame(np.random.rand(10,5),
columns=["x", "y", "col1", "col2", "col3"])
fig, axes = plt.subplots(nrows=3)
for ax, col in zip(axes, df.columns[2:]):
ax.scatter(df.x, df.y, c=df[col])
plt.show()
For categorical data it is often easier to plot several scatter plots, one per category.
import pandas as pd
import numpy as np; np.random.seed(42)
import matplotlib.pyplot as plt
import seaborn as sns
xx = np.random.rand(10,2)
cat1 = np.array(['cat','dog','dog','dog','cat','hamster','cat','cat','hamster','dog'])
cat2 = np.array(['blond','brown','brown','black','black','blond','blond','blond','brown','blond'])
d = {'x':xx[:,0], 'y':xx[:,1], 'pet':cat1, 'hair':cat2}
df = pd.DataFrame(data=d)
cols = ['pet',"hair"]
fig, axes = plt.subplots(nrows=len(cols ))
for ax,col in zip(axes,cols):
for n, group in df.groupby(col):
ax.scatter(group.x,group.y, label=n)
ax.legend()
plt.show()
You may surely use a FacetGrid, if you really want, but that requires a different data format of the DataFrame.
import pandas as pd
import numpy as np; np.random.seed(42)
import matplotlib.pyplot as plt
import seaborn as sns
xx = np.random.rand(10,2)
cat1 = np.array(['cat','dog','dog','dog','cat','hamster','cat','cat','hamster','dog'])
cat2 = np.array(['blond','brown','brown','black','black','blond','blond','blond','brown','blond'])
d = {'x':xx[:,0], 'y':xx[:,1], 'pet':cat1, 'hair':cat2}
df = pd.DataFrame(data=d)
df2 = pd.melt(df, id_vars=['x','y'], value_name='category', var_name="kind")
fg = sns.FacetGrid(data=df2, row="kind",hue='category', size=3)
fg.map(plt.scatter, 'x', 'y').add_legend()
I would like to create the following histogram (see image below) taken from the book "Think Stats". However, I cannot get them on the same plot. Each DataFrame takes its own subplot.
I have the following code:
import nsfg
import matplotlib.pyplot as plt
df = nsfg.ReadFemPreg()
preg = nsfg.ReadFemPreg()
live = preg[preg.outcome == 1]
first = live[live.birthord == 1]
others = live[live.birthord != 1]
#fig = plt.figure()
#ax1 = fig.add_subplot(111)
first.hist(column = 'prglngth', bins = 40, color = 'teal', \
alpha = 0.5)
others.hist(column = 'prglngth', bins = 40, color = 'blue', \
alpha = 0.5)
plt.show()
The above code does not work when I use ax = ax1 as suggested in: pandas multiple plots not working as hists nor this example does what I need: Overlaying multiple histograms using pandas. When I use the code as it is, it creates two windows with histograms. Any ideas how to combine them?
Here's an example of how I'd like the final figure to look:
As far as I can tell, pandas can't handle this situation. That's ok since all of their plotting methods are for convenience only. You'll need to use matplotlib directly. Here's how I do it:
%matplotlib inline
import numpy as np
import matplotlib.pyplot as plt
import pandas
#import seaborn
#seaborn.set(style='ticks')
np.random.seed(0)
df = pandas.DataFrame(np.random.normal(size=(37,2)), columns=['A', 'B'])
fig, ax = plt.subplots()
a_heights, a_bins = np.histogram(df['A'])
b_heights, b_bins = np.histogram(df['B'], bins=a_bins)
width = (a_bins[1] - a_bins[0])/3
ax.bar(a_bins[:-1], a_heights, width=width, facecolor='cornflowerblue')
ax.bar(b_bins[:-1]+width, b_heights, width=width, facecolor='seagreen')
#seaborn.despine(ax=ax, offset=10)
And that gives me:
In case anyone wants to plot one histogram over another (rather than alternating bars) you can simply call .hist() consecutively on the series you want to plot:
%matplotlib inline
import numpy as np
import matplotlib.pyplot as plt
import pandas
np.random.seed(0)
df = pandas.DataFrame(np.random.normal(size=(37,2)), columns=['A', 'B'])
df['A'].hist()
df['B'].hist()
This gives you:
Note that the order you call .hist() matters (the first one will be at the back)
A quick solution is to use melt() from pandas and then plot with seaborn.
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
# make dataframe
df = pd.DataFrame(np.random.normal(size=(200,2)), columns=['A', 'B'])
# plot melted dataframe in a single command
sns.histplot(df.melt(), x='value', hue='variable',
multiple='dodge', shrink=.75, bins=20);
Setting multiple='dodge' makes it so the bars are side-by-side, and shrink=.75 makes it so the pair of bars take up 3/4 of the whole bin.
To help understand what melt() did, these are the dataframes df and df.melt():
From the pandas website (http://pandas.pydata.org/pandas-docs/stable/visualization.html#visualization-hist):
df4 = pd.DataFrame({'a': np.random.randn(1000) + 1, 'b': np.random.randn(1000),
'c': np.random.randn(1000) - 1}, columns=['a', 'b', 'c'])
plt.figure();
df4.plot(kind='hist', alpha=0.5)
You make two dataframes and one matplotlib axis
import matplotlib.pyplot as plt
import pandas as pd
import numpy as np
df1 = pd.DataFrame({
'data1': np.random.randn(10),
'data2': np.random.randn(10)
})
df2 = df1.copy()
fig, ax = plt.subplots()
df1.hist(column=['data1'], ax=ax)
df2.hist(column=['data2'], ax=ax)
Here is the snippet, In my case I have explicitly specified bins and range as I didn't handle outlier removal as the author of the book.
fig, ax = plt.subplots()
ax.hist([first.prglngth, others.prglngth], 10, (27, 50), histtype="bar", label=("First", "Other"))
ax.set_title("Histogram")
ax.legend()
Refer Matplotlib multihist plot with different sizes example.
this could be done with brevity
plt.hist([First, Other], bins = 40, color =('teal','blue'), label=("First", "Other"))
plt.legend(loc='best')
Note that as the number of bins increase, it may become a visual burden.
You could also try to check out the pandas.DataFrame.plot.hist() function which will plot the histogram of each column of the dataframe in the same figure.
Visibility is limited though but you can check out if it helps!
https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.plot.hist.html