I have this code:
import pandas as pd
from pandas import datetime
from pandas import DataFrame as df
import matplotlib
from pandas_datareader import data as web
import matplotlib.pyplot as plt
import datetime
start = datetime.date(2016,1,1)
end = datetime.date.today()
stock = 'fb'
fig = plt.figure(dpi=1400)
data = web.DataReader(stock, 'yahoo', start, end)
fig, ax = plt.subplots(dpi=720)
data['vol_pct'] = data['Volume'].pct_change()
data.plot(y='vol_pct', ax = plt.gca(), title = 'this is the title \n second line')
ax.set(xlabel="Date")
ax.legend(loc='upper center', bbox_to_anchor=(0.32, -0.22), shadow=True, ncol=2)
plt.savefig('Test')
This is an example of another code but the problem is the same:
At bottom of the plot you can see that the legend is being cut out. In another plot of a different code which i am working on, even the ylabel is also cut when i save the plot using plt.savefig('Test').How can i can fix this?
It's a long-standing issue with .savefig() that it doesn't check legend and axis locations before setting bounds. As a rule, I solve this with the bbox_inches argument:
plt.savefig('Test', bbox_inches='tight')
This is similar to calling plt.tight_layout(), but takes all of the relevant artists into account, whereas tight_layout will often pull some objects into frame while cutting off new ones.
I have to tell pyplot to keep it tight more than half the time, so I'm not sure why this isn't the default behavior.
plt.subplots_adjust(bottom=0.4 ......)
I think this modification will satisfy you.
Or maybe you can relocate the legend to loc="upper left"
https://matplotlib.org/api/_as_gen/matplotlib.pyplot.subplots_adjust.html
please also checked this issue which raised 8 years ago..
Moving matplotlib legend outside of the axis makes it cutoff by the figure box
Related
I have a time-series dataframe with missing data for some time period. I would like to create a line plot and break a line where there is missing data.
data_site1_ave[["samples", "lkt"]].plot(figsize=(15,4), title = "Site 1", xlabel='')
Is it possible to create a gap, let's say from 2018-05-01 to 2018-10-30 in the line plot?
Yes, you can create arbitrary gaps by simply calling df.plot() several times, on the appropriate slices of the full dataframe. To make everything appear in the same plot, you can pass the ax keyword argument to plt.plot() via the df.plot() method. Turn the legend off for all but one call, so that the legend only has the one entry.
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
# create sample time series
N = 365
np.random.seed(42)
x = pd.date_range('2018-01-01', freq='d', periods=N)
y = np.cumsum(np.random.rand(N, 1) - 0.5)
df = pd.DataFrame(y, columns=['y'], index=x)
# plot time series with gap
fig, ax = plt.subplots()
df.loc[:'2018-05-01'].plot(ax=ax, c='blue')
df.loc['2018-10-31':].plot(ax=ax, c='blue', legend=False);
I know this exact question has been asked here, however the current solution does nothing for me. I can't seem to generate a legend that has a different color for each label. I have tried the current documentation on Matplotlib to no avail. I keep getting the error that my PathCollection object has no attribute legend_elements
EDIT: Also, I want my legend to be just the Years, unique years for the plot not how it is right now with is that each data point is mapped to my legend.
Here's what I have
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import matplotlib.pyplot as plt
from matplotlib.pyplot import figure
from matplotlib.pyplot import legend
import os
%config InlineBackend.figure_format = 'retina'
path = None
for dirname, _, filenames in os.walk('/kaggle/input'):
for filename in filenames:
path = os.path.join(dirname, filename)
# Indexes to be removed
early_demo_dividend = 13
high_income = 24
lower_middle_income = 40
north_america = 46
members = 50
post_demo = 56
_removals = [early_demo_dividend, high_income, lower_middle_income, north_america, members, post_demo]
#Read in data
df = pd.read_csv(path)
#Get the rows we want
df = df.loc[df['1960'] > 1]
df = df.drop(columns=["Code", "Type", "Indicator Name"])
#Remove the odd rows
for i in _removals:
df = df.drop(df.index[i])
#Format the dataframe
df = df.melt('Name', var_name='Year', value_name='Budget')
#Plot setup
plt.figure().set_size_inches(16,6)
plt.xticks(rotation=90)
plt.grid(True)
#Plot labels
plt.title('Military Spending of Countries')
plt.xlabel('Countries')
plt.ylabel('Budget in Billions')
#Plot data
new_year = df['Year'].astype(int)
scatter = plt.scatter(df['Name'], df['Budget'], c=(new_year / 10000) , label=new_year)
#Legend setup produce a legend with the unique colors from the scatter
legend1 = plt.legend(*scatter.legend_elements(),
loc="lower left", title="Years")
plt.add_artist(legend1)
plt.show()
Heres my plot
I also encountered this problem.
Try to upgrade your matplotlib with pip3 install --upgrade matplotlib
Uninstalling matplotlib-3.0.3:
Successfully uninstalled matplotlib-3.0.3
Successfully installed matplotlib-3.1.2
It works for me.
Despite the fact that my answer may not be relevant to the current question, I decided to leave it to describe my case - it might be useful to someone else:
When using matplotlib functions such as scatter or plot, incorrectly specify the name of some additional arguments, you can get the same error.
Example:
x = list(range(10))
y = list(range(10))
plt.scatter(x, y, labels='RESULT')
I get the error:
AttributeError: 'PathCollection' object has no property 'labels'
As it said in error message (but it is not obvious to an inattentive developer :) ):
the problem that I use labels instead of label
I am using stripplot in python where in axis I have time range from 3601 to 8600 in seconds. But from figure It's hard to see the time in x-axis. How can I add specific interval (like the y-axis in my figure) to make it visible? Here is my code to generate the figure.
import seaborn as sns; sns.set(color_codes=True)
# Visualising the plots
fig, ax = plt.subplots(figsize=(10,7))
params_anno = dict(jitter=0.25,size=8, color='#91bfdb', edgecolor='black', linewidth=1, dodge=False)
ax=sns.stripplot(x=dataset["Time"], y=dataset["Ob7"],**params_anno)
params_anno = dict(data=dataset_2, x='Time', y='Ob7',jitter=0.25)
ax=sns.stripplot(size=8,color='red', edgecolor='black',linewidth=1,**params_anno)
Here is my figure
I have seen seaborn.stripplot documentations but couldn't find any suitable example. Does anybody help?
The question is alredy 5months old, but if someone need answer.
You can try this:
from matplotlib import pyplot as plt
import matplotlib.ticker as ticker
fig,axs = plt.subplots()
axs.yaxis.set_major_locator(ticker.MultipleLocator(0.05))
The following code is an attempt to put points on a map via mplleaflet in a Jupyter notebook. It works for the first 3 points but not when including the 4th. It must be something other than this point - I can plot the 4th and 5th together for example. I want to be able to plot all the points including after the pound signs. Any ideas what's going wrong?
%matplotlib inline
import mplleaflet
import matplotlib.pyplot as plt
lats = [54.3256, 53.2692, 53.8242, 53.2178] #, 51.9978, 52.42, 53.1658, 54.292, 52.127, 51.505, 51.478, 51.35]
lons = [2.9356, 3.6278, 2.9453, 3.2203] #, 3.275, -1.83, 0.5239, -1.535, 0.956, -1.993, -0.461, 1.3667]
plt.hold(True)
plt.plot(lons, lats, 'rs')
mplleaflet.display()
EDIT: I've given up trying to use plt.scatter as this does not seem to work at all.
EDIT 2: seems I just needed to get rid of the 'mplleaflet.display()' suggested in the original code I was trying to make work. Hold is now depreciated however - see alternative below.
Try this:
import mplleaflet
import matplotlib.pyplot as plt
lats = [54.3256, 53.2692, 53.8242, 53.2178]
lons = [2.9356, 3.6278, 2.9453, 3.2203]
fig = plt.figure() #This is missing in your code.
plt.plot(lons, lats, 'r.')
#And after this call the funtion:
mplleaflet.display(fig=fig)
#It will display the matplotlib object created by plot function
I just discovered something really strange when using plot method of pandas.DataFrame. I am using pandas 0.19.1. Here is my MWE:
import numpy as np
import matplotlib.pyplot as plt
import matplotlib.dates as mdates
import pandas as pd
t = pd.date_range('1990-01-01', '1990-01-08', freq='1H')
x = pd.DataFrame(np.random.rand(len(t)), index=t)
fig, axe = plt.subplots()
x.plot(ax=axe)
plt.show(axe)
xt = axe.get_xticks()
When I try to format my xticklabels I get strange beahviours, then I insepcted objects to understand and I have found the following:
t[-1] - t[0] = Timedelta('7 days 00:00:00'), confirming the DateTimeIndex is what I expect;
xt = [175320, 175488], xticks are integers but they are not equals to a number of days since epoch (I do not have any idea about what it is);
xt[-1] - xt[0] = 168 there are more like index, there is the same amount that len(x) = 169.
This explains why I cannot succed to format my axe using:
axe.xaxis.set_major_locator(mdates.HourLocator(byhour=(0,6,12,18)))
axe.xaxis.set_major_formatter(mdates.DateFormatter("%a %H:%M"))
The first raise an error that there is to many ticks to generate
The second show that my first tick is Fri 00:00 but it should be Mon 00:00 (in fact matplotlib assumes the first tick to be 0481-01-03 00:00, oops this is where my bug is).
It looks like there is some incompatibility between pandas and matplotlib integer to date conversion but I cannot find out how to fix this issue.
If I run instead:
fig, axe = plt.subplots()
axe.plot(x)
axe.xaxis.set_major_formatter(mdates.DateFormatter("%a %H:%M"))
plt.show(axe)
xt = axe.get_xticks()
Everything works as expected but I miss all cool features from pandas.DataFrame.plot method such as curve labeling, etc. And here xt = [726468. 726475.].
How can I properly format my ticks using pandas.DataFrame.plot method instead of axe.plot and avoiding this issue?
Update
The problem seems to be about origin and scale (units) of underlying numbers for date representation. Anyway I cannot control it, even by forcing it to the correct type:
t = pd.date_range('1990-01-01', '1990-01-08', freq='1H', origin='unix', units='D')
There is a discrepancy between matplotlib and pandas representation. And I could not find any documentation of this problem.
Is this what you are going for? Note I shortened the date_range to make it easier to see the labels.
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import matplotlib.dates as dates
t = pd.date_range('1990-01-01', '1990-01-04', freq='1H')
x = pd.DataFrame(np.random.rand(len(t)), index=t)
# resample the df to get the index at 6-hour intervals
l = x.resample('6H').first().index
# set the ticks when you plot. this appears to position them, but not set the label
ax = x.plot(xticks=l)
# set the display value of the tick labels
ax.set_xticklabels(l.strftime("%a %H:%M"))
# hide the labels from the initial pandas plot
ax.set_xticklabels([], minor=True)
# make pretty
ax.get_figure().autofmt_xdate()
plt.show()