so I am plotting error bar of pandas dataframe. Now the error bar has a weird arrow at the top, but what I want is a horizontal line. For example, a figure like this:
But now my error bar ends with arrow instead of a horinzontal line.
Here is the code i used to generate it:
plot = meansum.plot(
kind="bar",
yerr=stdsum,
colormap="OrRd_r",
edgecolor="black",
grid=False,
figsize=(8, 2),
ax=ax,
position=0.45,
error_kw=dict(ecolor="black", elinewidth=0.5, lolims=True, marker="o"),
width=0.8,
)
So what should I change to make the error become the one I want. Thx.
Using plt.errorbar from matplotlib makes it easier as it returns several objects including the caplines which contain the marker you want to change (the arrow which is automatically used when lolims is set to True, see docs).
Using pandas, you just need to dig the correct line in the children of plot and change its marker:
import pandas as pd
import matplotlib.pyplot as plt
fig, ax = plt.subplots()
df = pd.DataFrame({"val":[1,2,3,4],"error":[.4,.3,.6,.9]})
meansum = df["val"]
stdsum = df["error"]
plot = meansum.plot(kind='bar',yerr=stdsum,colormap='OrRd_r',edgecolor='black',grid=False,figsize=8,2),ax=ax,position=0.45,error_kw=dict(ecolor='black',elinewidth=0.5, lolims=True),width=0.8)
for ch in plot.get_children():
if str(ch).startswith('Line2D'): # this is silly, but it appears that the first Line in the children are the caplines...
ch.set_marker('_')
ch.set_markersize(10) # to change its size
break
plt.show()
The result looks like:
Just don't set lolim = True and you are good to go, an example with sample data:
import pandas as pd
import matplotlib.pyplot as plt
fig, ax = plt.subplots()
df = pd.DataFrame({"val":[1,2,3,4],"error":[.4,.3,.6,.9]})
meansum = df["val"]
stdsum = df["error"]
plot = meansum.plot(kind='bar',yerr=stdsum,colormap='OrRd_r',edgecolor='black',grid=False,figsize=(8,2),ax=ax,position=0.45,error_kw=dict(ecolor='black',elinewidth=0.5),width=0.8)
plt.show()
Related
i just stumpled upon a problem I simply cannot solve. I have a dataset with raw data which I will upload here: https://file.io/oJqkZjAGyqV1
Its an excel file with the data inside.
I then created some code to open it, read it, generate a mean and sem of my data as below.
# Import required packages
import pandas as pd
import matplotlib as mpl
import matplotlib.pyplot as plt
import numpy as np
from pylab import cm
df = pd.read_excel("Chlorophyll_data_mod.xlsx")
#----Calculation of meanvalues and sem from raw_data---------
meandf2 = df.set_index(["Group"])
sets = []
for x in ["A","B","AB","xc"]:
meandf3 = meandf2.filter(like=f"Chl_{x}_").reset_index()
sets.append(meandf3)
#---------Grouping DataFrame----------#
means = []
ster = []
for x in range(len(sets)):
meandf = sets[x].groupby(["Group"]).mean()
meandf = meandf.reset_index()
means.append(meandf)
sems = sets[x].groupby("Group").sem()
sems = sems.reset_index()
ster.append(sems)
#----Selecting Dataframe from List-----#
plotdf = means[0]
ploter = ster[0]
plotgroup = plotdf.iloc[:,[0,]]
plotdata = plotdf.iloc[:,[1,]]
grouparray = plotgroup.to_numpy()
dataarray = plotdata.to_numpy()
#-----CreatePlot------#
fig, ax = plt.subplots(nrows=3, ncols=1, sharex="all", figsize=(10,8))
plotdf.plot(ax=ax[0,],x="Group",y="Chl_A_0D", kind="bar", legend=False, color="black")
plt.errorbar(x=plotdf["Group"], y=plotdf["Chl_A_0D"],yerr=ploter["Chl_A_0D"])
plotdf.plot(ax=ax[1,],x="Group",y="Chl_A_10DaT", kind="bar", legend=False, color="blue")
plt.errorbar(x=plotdf["Group"], y=plotdf["Chl_A_10DaT"],yerr=ploter["Chl_A_10DaT"])
plotdf.plot(ax=ax[2,],x="Group",y="Chl_A_7DaR", kind="bar", legend=False, color="magenta")
plt.errorbar(x=plotdf["Group"], y=plotdf["Chl_A_7DaR"],yerr=ploter["Chl_A_7DaR"])
#----Legend of the Plot-----#
fig.legend(loc="lower center", bbox_to_anchor=(0.5,0), fancybox=True, ncol=6)
#----Layout------#
plt.tight_layout(rect=[0, 0.02, 1,1])
plt.show()
And I manage to create a subplot, which shows 3 of my interested data points. However, I struggle with the error bars.
My approach was to calculate the sem and store it into a new dataframe. And then just read it from there for the yerr. However, this doesn't work.
plotdf.plot(ax=ax[2,],x="Group",y="Chl_A_7DaR", kind="bar", legend=False, color="magenta", yerr=ploter["Chl_A_7DaR"])
Results in an array error because of the structure.
And my current approach, as in the main code above only draws the error bars in the last subplot, but not in each individual plot.
Maybe here is someone who could help me understanding this function?
Best regards
I am plotting multiple dataframes as point plot using seaborn. Also I am plotting all the dataframes on the same axis.
How would I add legend to the plot ?
My code takes each of the dataframe and plots it one after another on the same figure.
Each dataframe has same columns
date count
2017-01-01 35
2017-01-02 43
2017-01-03 12
2017-01-04 27
My code :
f, ax = plt.subplots(1, 1, figsize=figsize)
x_col='date'
y_col = 'count'
sns.pointplot(ax=ax,x=x_col,y=y_col,data=df_1,color='blue')
sns.pointplot(ax=ax,x=x_col,y=y_col,data=df_2,color='green')
sns.pointplot(ax=ax,x=x_col,y=y_col,data=df_3,color='red')
This plots 3 lines on the same plot. However the legend is missing. The documentation does not accept label argument .
One workaround that worked was creating a new dataframe and using hue argument.
df_1['region'] = 'A'
df_2['region'] = 'B'
df_3['region'] = 'C'
df = pd.concat([df_1,df_2,df_3])
sns.pointplot(ax=ax,x=x_col,y=y_col,data=df,hue='region')
But I would like to know if there is a way to create a legend for the code that first adds sequentially point plot to the figure and then add a legend.
Sample output :
I would suggest not to use seaborn pointplot for plotting. This makes things unnecessarily complicated.
Instead use matplotlib plot_date. This allows to set labels to the plots and have them automatically put into a legend with ax.legend().
import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
import numpy as np
date = pd.date_range("2017-03", freq="M", periods=15)
count = np.random.rand(15,4)
df1 = pd.DataFrame({"date":date, "count" : count[:,0]})
df2 = pd.DataFrame({"date":date, "count" : count[:,1]+0.7})
df3 = pd.DataFrame({"date":date, "count" : count[:,2]+2})
f, ax = plt.subplots(1, 1)
x_col='date'
y_col = 'count'
ax.plot_date(df1.date, df1["count"], color="blue", label="A", linestyle="-")
ax.plot_date(df2.date, df2["count"], color="red", label="B", linestyle="-")
ax.plot_date(df3.date, df3["count"], color="green", label="C", linestyle="-")
ax.legend()
plt.gcf().autofmt_xdate()
plt.show()
In case one is still interested in obtaining the legend for pointplots, here a way to go:
sns.pointplot(ax=ax,x=x_col,y=y_col,data=df1,color='blue')
sns.pointplot(ax=ax,x=x_col,y=y_col,data=df2,color='green')
sns.pointplot(ax=ax,x=x_col,y=y_col,data=df3,color='red')
ax.legend(handles=ax.lines[::len(df1)+1], labels=["A","B","C"])
ax.set_xticklabels([t.get_text().split("T")[0] for t in ax.get_xticklabels()])
plt.gcf().autofmt_xdate()
plt.show()
Old question, but there's an easier way.
sns.pointplot(x=x_col,y=y_col,data=df_1,color='blue')
sns.pointplot(x=x_col,y=y_col,data=df_2,color='green')
sns.pointplot(x=x_col,y=y_col,data=df_3,color='red')
plt.legend(labels=['legendEntry1', 'legendEntry2', 'legendEntry3'])
This lets you add the plots sequentially, and not have to worry about any of the matplotlib crap besides defining the legend items.
I tried using Adam B's answer, however, it didn't work for me. Instead, I found the following workaround for adding legends to pointplots.
import matplotlib.patches as mpatches
red_patch = mpatches.Patch(color='#bb3f3f', label='Label1')
black_patch = mpatches.Patch(color='#000000', label='Label2')
In the pointplots, the color can be specified as mentioned in previous answers. Once these patches corresponding to the different plots are set up,
plt.legend(handles=[red_patch, black_patch])
And the legend ought to appear in the pointplot.
This goes a bit beyond the original question, but also builds on #PSub's response to something more general---I do know some of this is easier in Matplotlib directly, but many of the default styling options for Seaborn are quite nice, so I wanted to work out how you could have more than one legend for a point plot (or other Seaborn plot) without dropping into Matplotlib right at the start.
Here's one solution:
import numpy as np
import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt
# We will need to access some of these matplotlib classes directly
from matplotlib.lines import Line2D # For points and lines
from matplotlib.patches import Patch # For KDE and other plots
from matplotlib.legend import Legend
from matplotlib import cm
# Initialise random number generator
rng = np.random.default_rng(seed=42)
# Generate sample of 25 numbers
n = 25
clusters = []
for c in range(0,3):
# Crude way to get different distributions
# for each cluster
p = rng.integers(low=1, high=6, size=4)
df = pd.DataFrame({
'x': rng.normal(p[0], p[1], n),
'y': rng.normal(p[2], p[3], n),
'name': f"Cluster {c+1}"
})
clusters.append(df)
# Flatten to a single data frame
clusters = pd.concat(clusters)
# Now do the same for data to feed into
# the second (scatter) plot...
n = 8
points = []
for c in range(0,2):
p = rng.integers(low=1, high=6, size=4)
df = pd.DataFrame({
'x': rng.normal(p[0], p[1], n),
'y': rng.normal(p[2], p[3], n),
'name': f"Group {c+1}"
})
points.append(df)
points = pd.concat(points)
# And create the figure
f, ax = plt.subplots(figsize=(8,8))
# The KDE-plot generates a Legend 'as usual'
k = sns.kdeplot(
data=clusters,
x='x', y='y',
hue='name',
shade=True,
thresh=0.05,
n_levels=2,
alpha=0.2,
ax=ax,
)
# Notice that we access this legend via the
# axis to turn off the frame, set the title,
# and adjust the patch alpha level so that
# it closely matches the alpha of the KDE-plot
ax.get_legend().set_frame_on(False)
ax.get_legend().set_title("Clusters")
for lh in ax.get_legend().get_patches():
lh.set_alpha(0.2)
# You would probably want to sort your data
# frame or set the hue and style order in order
# to ensure consistency for your own application
# but this works for demonstration purposes
groups = points.name.unique()
markers = ['o', 'v', 's', 'X', 'D', '<', '>']
colors = cm.get_cmap('Dark2').colors
# Generate the scatterplot: notice that Legend is
# off (otherwise this legend would overwrite the
# first one) and that we're setting the hue, style,
# markers, and palette using the 'name' parameter
# from the data frame and the number of groups in
# the data.
p = sns.scatterplot(
data=points,
x="x",
y="y",
hue='name',
style='name',
markers=markers[:len(groups)],
palette=colors[:len(groups)],
legend=False,
s=30,
alpha=1.0
)
# Here's the 'magic' -- we use zip to link together
# the group name, the color, and the marker style. You
# *cannot* retreive the marker style from the scatterplot
# since that information is lost when rendered as a
# PathCollection (as far as I can tell). Anyway, this allows
# us to loop over each group in the second data frame and
# generate a 'fake' Line2D plot (with zero elements and no
# line-width in our case) that we can add to the legend. If
# you were overlaying a line plot or a second plot that uses
# patches you'd have to tweak this accordingly.
patches = []
for x in zip(groups, colors[:len(groups)], markers[:len(groups)]):
patches.append(Line2D([0],[0], linewidth=0.0, linestyle='',
color=x[1], markerfacecolor=x[1],
marker=x[2], label=x[0], alpha=1.0))
# And add these patches (with their group labels) to the new
# legend item and place it on the plot.
leg = Legend(ax, patches, labels=groups,
loc='upper left', frameon=False, title='Groups')
ax.add_artist(leg);
# Done
plt.show();
Here's the output:
I am trying to plot two different variables (linked by a relation of causality), delai_jour and date_sondage on a single FacetGrid. I can do it with this code:
g = sns.FacetGrid(df_verif_sum, col="prefecture", col_wrap=2, aspect=2, sharex=True,)
g = g.map(plt.plot, "date_sondage", "delai_jour", color="m", linewidth=2)
g = g.map(plt.bar, "date_sondage", "impossible")
which gives me this:
FacetGrid
(There are 33 of them in total).
I'm interested in comparing the patterns across the various prefecture, but due to the difference in magnitude I cannot see the changes in the line chart.
For this specific work, the best way to do it is to create a secondary y axis, but I can't seem to make anything work: it doesn't look like it's possible with FacetGrid, and I didn't understand the code not was able to replicate the examples i've seen with pure matplotlib.
How should I go about it?
I got this to work by iterating through the axes and plotting a secondary axis as in a typical Seaborn graph.
Using the OP example:
g = sns.FacetGrid(df_verif_sum, col="prefecture", col_wrap=2, aspect=2, sharex=True)
g = g.map(plt.plot, "date_sondage", "delai_jour", color="m", linewidth=2)
for ax, (_, subdata) in zip(g.axes, df_verif_sum.groupby('prefecture')):
ax2=ax.twinx()
subdata.plot(x='data_sondage',y='impossible', ax=ax2,legend=False,color='r')
If you do any formatting to the x-axis, you may have to do it to both ax and ax2.
Here's an example where you apply a custom mapping function to the dataframe of interest. Within the function, you can call plt.gca() to get the current axis at the facet being currently plotted in FacetGrid. Once you have the axis, twinx() can be called just like you would in plain old matplotlib plotting.
import matplotlib.pyplot as plt
import pandas as pd
import numpy as np
import seaborn as sns
def facetgrid_two_axes(*args, **kwargs):
data = kwargs.pop('data')
dual_axis = kwargs.pop('dual_axis')
alpha = kwargs.pop('alpha', 0.2)
kwargs.pop('color')
ax = plt.gca()
if dual_axis:
ax2 = ax.twinx()
ax2.set_ylabel('Second Axis!')
ax.plot(data['x'],data['y1'], **kwargs, color='red',alpha=alpha)
if dual_axis:
ax2.plot(df['x'],df['y2'], **kwargs, color='blue',alpha=alpha)
df = pd.DataFrame()
df['x'] = np.arange(1,5,1)
df['y1'] = 1 / df['x']
df['y2'] = df['x'] * 100
df['facet'] = 'foo'
df2 = df.copy()
df2['facet'] = 'bar'
df3 = pd.concat([df,df2])
win_plot = sns.FacetGrid(df3, col='facet', size=6)
(win_plot.map_dataframe(facetgrid_two_axes, dual_axis=True)
.set_axis_labels("X", "First Y-axis"))
plt.show()
This isn't the prettiest plot as you might want to adjust the presence of the second y-axis' label, the spacing between plots, etc. but the code suffices to show how to plot two series of differing magnitudes within FacetGrids.
I have the following plot build with seaborn using factorplot() method.
Is it possible to use the line style as a legend to replace the legend based on line color on the right?
graycolors = sns.mpl_palette('Greys_r', 4)
g = sns.factorplot(x="k", y="value", hue="class", palette=graycolors,
data=df, linestyles=["-", "--"])
Furthermore I'm trying to get both lines in black color using the color="black" parameter in my factorplot method but this results in an exception "factorplot() got an unexpected keyword argument 'color'". How can I paint both lines in the same color and separate them by the linestyle only?
I have been looking for a solution trying to put the linestyle in the legend like matplotlib, but I have not yet found how to do this in seaborn. However, to make the data clear in the legend I have used different markers:
import seaborn as sns
import numpy as np
import pandas as pd
# creating some data
n = 11
x = np.linspace(0,2, n)
y = np.sin(2*np.pi*x)
y2 = np.cos(2*np.pi*x)
data = {'x': np.append(x, x), 'y': np.append(y, y2),
'class': np.append(np.repeat('sin', n), np.repeat('cos', n))}
df = pd.DataFrame(data)
# plot the data with the markers
# note that I put the legend=False to move it up (otherwise it was blocking the graph)
g=sns.factorplot(x="x", y="y", hue="class", palette=graycolors,
data=df, linestyles=["-", "--"], markers=['o','v'], legend=False)
# placing the legend up
g.axes[0][0].legend(loc=1)
# showing graph
plt.show()
you can try the following:
h = plt.gca().get_lines()
lg = plt.legend(handles=h, labels=['YOUR Labels List'], loc='best')
It worked fine with me.
The following snippet of code is creating a candlestick chart with 4 price bars. The lines of code written between the "NOT WORKING" tags are supposed to annotate the word 'BUY' on the second price bar following the coordinates stored into the variables d (x-axis) and h (y-axis). However, this does not work cause there's no annotation into the chart.
The code below is runnable, can anyone explain me how to make an annotation on a chart like this?
from pylab import *
from matplotlib.finance import candlestick
import matplotlib.gridspec as gridspec
quotes = [(734542.0, 1.326, 1.3287, 1.3322, 1.3215), (734543.0, 1.3286, 1.3198, 1.3292, 1.3155), (734546.0, 1.321, 1.3187, 1.3284, 1.3186), (734547.0, 1.3186, 1.3133, 1.3217, 1.308)]
fig, ax = subplots()
candlestick(ax,quotes,width = 0.5)
ax.xaxis_date()
ax.autoscale_view()
#NOT WORKING
h = quotes[1][3]
d = quotes[1][0]
ax.annotate('BUY', xy = (d-1,h), xytext = (d-1, h+0.5), arrowprops = dict(facecolor='black',width=1,shrink=0.25))
#NOT WORKING
plt.show()
P.S. Embedding the statement print "(", d, ",", h, ")" gives the following output: >>>
( 734543.0 , 1.3292 ). That is exactly the point where I would like to get my arrow, so I guess the problem must be related with the visualization of the arrow and not with its creation.
Your problem is that your arrow is effectively off the matplotlib screen. You have set the xytext position to (d-1, h+0.5) which is way, way off your y-limits. The following fixes your code:
from pylab import *
from matplotlib.finance import candlestick
import matplotlib.gridspec as gridspec
quotes = [(734542.0, 1.326, 1.3287, 1.3322, 1.3215), (734543.0, 1.3286, 1.3198, 1.3292, 1.3155), (734546.0, 1.321, 1.3187, 1.3284, 1.3186), (734547.0, 1.3186, 1.3133, 1.3217, 1.308)]
fig, ax = subplots()
candlestick(ax,quotes,width = 0.5)
ax.xaxis_date()
ax.autoscale_view()
#NOT WORKING
h = quotes[1][3]
d = quotes[1][0]
ax.annotate('BUY', xy = (d-1,h), xytext = (d-1, h+0.003), arrowprops = dict(facecolor='black',width=1,shrink=0.25))
#NOT WORKING
plt.show()