So in Spyder IPython and in Jupyter notebook, the following code is failing to create subplots:
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
mydict = {'a': [1,2,3,4], 'b':[2,3,4,5], 'c':[3,4,5,6]}
df = pd.DataFrame(mydict)
fig, axes = plt.subplots(3,1)
axes[0] = plt.plot(df.a)
axes[1] = plt.plot(df.b)
axes[2] = plt.plot(df.c)
plt.show(fig)
and it gives back the following plot:
this also happens when I copy-c copy-vd the example code from the matplotlib webpage
what I would like is the three columns in the three different subplots to be plotted
If you create your axes using plt.subplots you are using the object oriented approach in matplotlib. Then you have to call plot() on the axes object, so axes[0].plot(df.a), not plt.plot.
What you are doing is a weird hybrid between the procedural and object oriented approach and you also overwrite the axes objects that you created when you write axes[0] = plt.plot(....
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
mydict = {'a': [1,2,3,4], 'b':[2,3,4,5], 'c':[3,4,5,6]}
df = pd.DataFrame(mydict)
fig, axes = plt.subplots(3,1)
axes[0].plot(df.a)
axes[1].plot(df.b)
axes[2].plot(df.c)
plt.show()
Related
I have lot of feature in data and i want to make box plot for each feature. So for that
import pandas as pd
import seaborn as sns
plt.figure(figsize=(25,20))
for data in train_df.columns:
plt.subplot(7,4,i+1)
plt.subplots_adjust(hspace = 0.5, wspace = 0.5)
ax =sns.boxplot(train_df[data])
I did this
and the output is
All the plot are on one image i want something like
( not with skew graphs but with box plot )
What changes i need to do ?
In your code, I cannot see where the i is coming from and also it's not clear how ax was assigned.
Maybe try something like this, first an example data frame:
import pandas as pd
import numpy as np
import seaborn as sns
from matplotlib import pyplot as plt
train_df = pd.concat([pd.Series(np.random.normal(i,1,100)) for i in range(12)],axis=1)
Set up fig and a flattened ax for each subplot:
fig,ax = plt.subplots(4,3,figsize=(10,10))
ax = ax.flatten()
The most basic would be to call sns.boxplot assigning ax inside the function:
for i,data in enumerate(train_df.columns):
sns.boxplot(train_df[data],ax=ax[i])
I 'm using Seaborn in a Jupyter notebook to plot histograms like this:
import numpy as np
import pandas as pd
from pandas import DataFrame
import matplotlib.pyplot as plt
import seaborn as sns
%matplotlib inline
df = pd.read_csv('CTG.csv', sep=',')
sns.distplot(df['LBE'])
I have an array of columns with values that I want to plot histogram for and I tried plotting a histogram for each of them:
continous = ['b', 'e', 'LBE', 'LB', 'AC']
for column in continous:
sns.distplot(df[column])
And I get this result - only one plot with (presumably) all histograms:
My desired result is multiple histograms that looks like this (one for each variable):
How can I do this?
Insert plt.figure() before each call to sns.distplot() .
Here's an example with plt.figure():
Here's an example without plt.figure():
Complete code:
# imports
import numpy as np
import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt
plt.rcParams['figure.figsize'] = [6, 2]
%matplotlib inline
# sample time series data
np.random.seed(123)
df = pd.DataFrame(np.random.randint(-10,12,size=(300, 4)), columns=list('ABCD'))
datelist = pd.date_range(pd.datetime(2014, 7, 1).strftime('%Y-%m-%d'), periods=300).tolist()
df['dates'] = datelist
df = df.set_index(['dates'])
df.index = pd.to_datetime(df.index)
df.iloc[0]=0
df=df.cumsum()
# create distplots
for column in df.columns:
plt.figure() # <==================== here!
sns.distplot(df[column])
Distplot has since been deprecated in seaborn versions >= 0.14.0. You can, however, use sns.histplot() to plot histogram distributions of the entire dataframe (numerical features only) in the following way:
fig, axes = plt.subplots(2,5, figsize=(15, 5))
ax = axes.flatten()
for i, col in enumerate(df.columns):
sns.histplot(df[col], ax=ax[i]) # histogram call
ax[i].set_title(col)
# remove scientific notation for both axes
ax[i].ticklabel_format(style='plain', axis='both')
fig.tight_layout(w_pad=6, h_pad=4) # change padding
plt.show()
If, you specifically want a way to estimate the probability density function of a continuous random variable using the Kernel Density Function (mimicing the default behavior of sns.distplot()), then inside the sns.histplot() function call, add kde=True, and you will have curves overlaying the histograms.
Also works when looping with plt.show() inside:
for column in df.columns:
sns.distplot(df[column])
plt.show()
When I do the following using Pandas on IPython, it only shows the last picture I drawn, is there a way I can let them show sequentially on IPython?
def drawBar(colName):
df1=df[colName].value_counts().plot(kind='bar', title=colName)
drawBar("myBiscuit")
drawBar("myBedRoom")
...(many more drawBar)
For plotting graphs in the notebook, you'd use the IPython magic %matplotlib inline.
(a) Plotting each individual graph one after the other:
You would need to call plt.show() for each graph. This will return a long list of plots in your IPython.
import matplotlib.pyplot as plt
import pandas as pd
import numpy as np
%matplotlib inline
colNames = "ABCDEFGHI"
x = np.random.randint(0,5, size=(10, 9))
df = pd.DataFrame(x, columns=[letter for letter in colNames])
def drawBar(colName):
df1=df[colName].value_counts().plot(kind='bar', title=colName)
for i in range(9):
drawBar(colNames[i])
plt.show()
(b) Using subplots.
Creating several subplots can be done with plt.subplots(). Then using the ax keyword argument to the pandas plotting function, creates the graph on the specified axes.
import matplotlib.pyplot as plt
import pandas as pd
import numpy as np
%matplotlib inline
colNames = "ABCDEFGHI"
x = np.random.randint(0,5, size=(10, 9))
df = pd.DataFrame(x, columns=[letter for letter in colNames])
fig, axes = plt.subplots(3,3)
def drawBar(colName, ax):
df1=df[colName].value_counts().plot(kind='bar', title=colName, ax=ax)
for i, ax in enumerate(axes.flatten()):
drawBar(colNames[i], ax)
plt.tight_layout()
I am running following code to draw histograms in 3 by 3 grid for 9 varaibles.However, it plots only one variable.
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
def draw_histograms(df, variables, n_rows, n_cols):
fig=plt.figure()
for i, var_name in enumerate(variables):
ax=fig.add_subplot(n_rows,n_cols,i+1)
df[var_name].hist(bins=10,ax=ax)
plt.title(var_name+"Distribution")
plt.show()
You're adding subplots correctly but you call plt.show for each added subplot which causes what has been drawn so far to be shown, i.e. one plot. If you're for instance plotting inline in IPython you will only see the last plot drawn.
Matplotlib provides some nice examples of how to use subplots.
Your problem is fixed like:
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
def draw_histograms(df, variables, n_rows, n_cols):
fig=plt.figure()
for i, var_name in enumerate(variables):
ax=fig.add_subplot(n_rows,n_cols,i+1)
df[var_name].hist(bins=10,ax=ax)
ax.set_title(var_name+" Distribution")
fig.tight_layout() # Improves appearance a bit.
plt.show()
test = pd.DataFrame(np.random.randn(30, 9), columns=map(str, range(9)))
draw_histograms(test, test.columns, 3, 3)
Which gives a plot like:
In case you don't really worry about titles, here's a one-liner
df = pd.DataFrame(np.random.randint(10, size=(100, 9)))
df.hist(color='k', alpha=0.5, bins=10)
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