Have a look at the graph below:
It's a subplot of this larger figure:
I see two problems with it. First, the x-axis labels overlap with one another (this is my major issue). Second. the location of the x-axis minor gridlines seems a bit wonky. On the left of the graph, they look properly spaced. But on the right, they seem to be crowding the major gridlines...as if the major gridline locations aren't proper multiples of the minor tick locations.
My setup is that I have a DataFrame called df which has a DatetimeIndex on the rows and a column called value which contains floats. I can provide an example of the df contents in a gist if necessary. A dozen or so lines of df are at the bottom of this post for reference.
Here's the code that produces the figure:
now = dt.datetime.now()
fig, axes = plt.subplots(2, 2, figsize=(15, 8), dpi=200)
for i, d in enumerate([360, 30, 7, 1]):
ax = axes.flatten()[i]
earlycut = now - relativedelta(days=d)
data = df.loc[df.index>=earlycut, :]
ax.plot(data.index, data['value'])
ax.xaxis_date()
ax.get_xaxis().set_minor_locator(mpl.ticker.AutoMinorLocator())
ax.get_yaxis().set_minor_locator(mpl.ticker.AutoMinorLocator())
ax.grid(b=True, which='major', color='w', linewidth=1.5)
ax.grid(b=True, which='minor', color='w', linewidth=0.75)
What is my best option here to get the x-axis labels to stop overlapping each other (in each of the four subplots)? Also, separately (but less urgently), what's up with the minor tick issue in the top-left subplot?
I am on Pandas 0.13.1, numpy 1.8.0, and matplotlib 1.4.x.
Here's a small snippet of df for reference:
id scale tempseries_id value
timestamp
2014-11-02 14:45:10.302204+00:00 7564 F 1 68.0000
2014-11-02 14:25:13.532391+00:00 7563 F 1 68.5616
2014-11-02 14:15:12.102229+00:00 7562 F 1 68.9000
2014-11-02 14:05:13.252371+00:00 7561 F 1 69.0116
2014-11-02 13:55:11.792191+00:00 7560 F 1 68.7866
2014-11-02 13:45:10.782227+00:00 7559 F 1 68.6750
2014-11-02 13:35:10.972248+00:00 7558 F 1 68.4500
2014-11-02 13:25:10.362213+00:00 7557 F 1 68.1116
2014-11-02 13:15:10.822247+00:00 7556 F 1 68.2250
2014-11-02 13:05:10.102200+00:00 7555 F 1 68.5616
2014-11-02 12:55:10.292217+00:00 7554 F 1 69.0116
2014-11-02 12:45:10.382226+00:00 7553 F 1 69.3500
2014-11-02 12:35:10.642245+00:00 7552 F 1 69.2366
2014-11-02 12:25:12.642255+00:00 7551 F 1 69.1250
2014-11-02 12:15:11.122382+00:00 7550 F 1 68.7866
2014-11-02 12:05:11.332224+00:00 7549 F 1 68.5616
2014-11-02 11:55:11.662311+00:00 7548 F 1 68.2250
2014-11-02 11:45:11.122193+00:00 7547 F 1 68.4500
2014-11-02 11:35:11.162271+00:00 7546 F 1 68.7866
2014-11-02 11:25:12.102211+00:00 7545 F 1 69.2366
2014-11-02 11:15:10.422226+00:00 7544 F 1 69.4616
2014-11-02 11:05:11.412216+00:00 7543 F 1 69.3500
2014-11-02 10:55:10.772212+00:00 7542 F 1 69.1250
2014-11-02 10:45:11.332220+00:00 7541 F 1 68.7866
2014-11-02 10:35:11.332232+00:00 7540 F 1 68.5616
2014-11-02 10:25:11.202411+00:00 7539 F 1 68.2250
2014-11-02 10:15:11.932326+00:00 7538 F 1 68.5616
2014-11-02 10:05:10.922229+00:00 7537 F 1 68.9000
2014-11-02 09:55:11.602357+00:00 7536 F 1 69.3500
Edit: Trying fig.autofmt_xdate():
I don't think this going to do the trick. This seems to use the same x-tick labels for both graphs on the left and also for both graphs on the right. Which is not correct given my data. Please see the problematic output below:
Ok, finally got it working. The trick was to use plt.setp to manually rotate the tick labels. Using fig.autofmt_xdate() did not work as it does some unexpected things when you have multiple subplots in your figure. Here's the working code with its output:
for i, d in enumerate([360, 30, 7, 1]):
ax = axes.flatten()[i]
earlycut = now - relativedelta(days=d)
data = df.loc[df.index>=earlycut, :]
ax.plot(data.index, data['value'])
ax.get_xaxis().set_minor_locator(mpl.ticker.AutoMinorLocator())
ax.get_yaxis().set_minor_locator(mpl.ticker.AutoMinorLocator())
ax.grid(b=True, which='major', color='w', linewidth=1.5)
ax.grid(b=True, which='minor', color='w', linewidth=0.75)
plt.setp(ax.get_xticklabels(), rotation=30, horizontalalignment='right')
fig.tight_layout()
By the way, the comment earlier about some matplotlib things taking forever is very interesting here. I'm using a raspberry pi to act as a weather station at a remote location. It's collecting the data and serving the results via the web. And boy oh boy, it's really wheezing trying to put out these graphics.
Due to the way text rendering is handled in matplotlib, auto-detecting overlapping text really slows things down. (The space that text takes up can't be accurately calculated until after it's been drawn.) For that reason, matplotlib doesn't try to do this automatically.
Therefore, it's best to rotate long tick labels. Because dates most commonly have this problem, there's a figure method fig.autofmt_xdate() that will (among other things) rotate the tick labels to make them a bit more readable. (Note: If you're using a pandas plot method, it returns an axes object, so you'll need to use ax.figure.autofmt_xdate().)
As a quick example:
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
time = pd.date_range('01/01/2014', '4/01/2014', freq='H')
values = np.random.normal(0, 1, time.size).cumsum()
fig, ax = plt.subplots()
ax.plot_date(time, values, marker='', linestyle='-')
fig.autofmt_xdate()
plt.show()
If we were to leave fig.autofmt_xdate() out:
And if we use fig.autofmt_xdate():
For the problems which don't have date values in x axis, rather a string, you can insert \n character in x axis values so they don't overlap. Here is an example -
The data frame is
somecol value
category 1 of column 16
category 2 of column 13
category 3 of column 21
category 4 of column 20
category 5 of column 11
category 6 of column 22
category 7 of column 19
category 8 of column 14
category 9 of column 18
category 10 of column 23
category 11 of column 10
category 12 of column 24
category 13 of column 17
category 14 of column 15
category 15 of column 12
I need to plot value on y axis and somecol on x axis, which will normally be plotted like this -
As you can see, there is a lot of overlap. Now introduce \n character in somecol column.
somecol = df['somecol'].values.tolist()
for i in range(len(somecol)):
x = somecol[i].split(' ')
# insert \n before 'of'
x.insert(x.index('of'),'\n')
somecol[i] = ' '.join(x)
Now if you plot, it will look like this -
plt.plot(somecol, df['val'])
This method works well if you don't want to rotate your labels.
The only con so far I found in this method is that you need to tweak your labels 3-4 times i.e., try with multiple formats to display the plot in best format.
Related
I have the code as below to plot multiple plots on the same figure
fig, ax = plt.subplots(figsize=(25, 10))
def wl_ratioplot(wavelength1,wavelength2, dataframe, x1=0.1,x2=1.5,y1=-500,y2=25000):
a=dataframe[['asphalt_index','layer_thickness',wavelength1,wavelength2]].copy()
sns.scatterplot(x=a[wavelength1]/a[wavelength2],y=a['layer_thickness'],data=a)
ax.set_xlim(x1,x2)
ax.set_ylim(y1,y2)
leg = "{} vs {}".format(wavelength1,wavelength2)
print(leg) #this line is only to see the variable legend has the proper content
ax.legend(leg)
wl_ratioplot(wave_lengths[2],wave_lengths[0],dataframe=train_df_wo_outliers,x1=-.1,x2=3)
wl_ratioplot(wave_lengths[0],wave_lengths[1],dataframe=train_df_wo_outliers,x1=-.1,x2=3)
wl_ratioplot(wave_lengths[3],wave_lengths[1],dataframe=train_df_wo_outliers,x1=-.1,x2=3)
wl_ratioplot(wave_lengths[3],wave_lengths[0],dataframe=train_df_wo_outliers,x1=-.1,x2=3)
wl_ratioplot(wave_lengths[2],wave_lengths[1],dataframe=train_df_wo_outliers,x1=-.1,x2=3)
I get the plot as the below pic where the legend seems to be first 5 letters separately even though the variable legend has the right content
There was another similar question & the solution was to put a square bracket to the variable legend. I tried this with the code as below.
fig, ax = plt.subplots(figsize=(25, 10))
def wl_ratioplot(wavelength1,wavelength2, dataframe, x1=0.1,x2=1.5,y1=-500,y2=25000):
a=dataframe[['asphalt_index','layer_thickness',wavelength1,wavelength2]].copy()
sns.scatterplot(x=a[wavelength1]/a[wavelength2],y=a['layer_thickness'],data=a)
ax.set_xlim(x1,x2)
ax.set_ylim(y1,y2)
leg = "{} vs {}".format(wavelength1,wavelength2)
print(leg)#this line is only to see the variable legend has the proper content
ax.legend([leg])
wl_ratioplot(wave_lengths[2],wave_lengths[0],dataframe=train_df_wo_outliers,x1=-.1,x2=3)
wl_ratioplot(wave_lengths[0],wave_lengths[1],dataframe=train_df_wo_outliers,x1=-.1,x2=3)
wl_ratioplot(wave_lengths[3],wave_lengths[1],dataframe=train_df_wo_outliers,x1=-.1,x2=3)
wl_ratioplot(wave_lengths[3],wave_lengths[0],dataframe=train_df_wo_outliers,x1=-.1,x2=3)
wl_ratioplot(wave_lengths[2],wave_lengths[1],dataframe=train_df_wo_outliers,x1=-.1,x2=3)
Now I get the full legend but only the first legend is shown as the pic below
Can someone let me know how to get the full legend for all the plots? Thanks.
dummy data (the plot in pic will NOT match)
14nm 15nm 16nm 17nm 18nm 19nm layer_thickness
1 2 3 4 5 6 0
1 2 3 4 5 6 0
3 5 7 9 11 13 5700
1 2 3 4 5 6 0
3 5 7 9 11 13 8600
1 2 3 4 5 6 0
3 5 7 9 11 13 5000
1 2 3 4 5 6 0
45 55 65 75 85 95 100
1 2 3 4 5 6 0
8 15 22 29 36 43 16600
wave_lengths=['15nm','16nm','14nm','18nm']
Answer Update
Based on answer from Quang Hoang. The output pics using scatter plot from matplotlib & sns.scatterplot
With plt it is pretty natural:
def wl_ratioplot(wavelength1,wavelength2, dataframe,
x1=0.1,x2=1.5,y1=-500,y2=25000,
ax=None):
leg = "{} vs {}".format(wavelength1,wavelength2)
# set the label here, and let plt deal with it
# also, you don't need to copy the dataframe:
ax.scatter(x=dataframe[wavelength1]/dataframe[wavelength2],
y=dataframe['layer_thickness'],label=leg)
ax.set_xlim(x1,x2)
ax.set_ylim(y1,y2)
fig, ax = plt.subplots(figsize=(25, 10))
wl_ratioplot(wave_lengths[2],wave_lengths[0],dataframe=df,x1=-.1,x2=3, ax=ax)
wl_ratioplot(wave_lengths[0],wave_lengths[1],dataframe=df,x1=-.1,x2=3, ax=ax)
wl_ratioplot(wave_lengths[3],wave_lengths[1],dataframe=df,x1=-.1,x2=3, ax=ax)
wl_ratioplot(wave_lengths[3],wave_lengths[0],dataframe=df,x1=-.1,x2=3, ax=ax)
wl_ratioplot(wave_lengths[2],wave_lengths[1],dataframe=df,x1=-.1,x2=3, ax=ax)
ax.legend()
Output:
every time you call the function wl_ratioplot the legend is being reset the final value. use a array to store all the legends then access it all through a loop.
ax.legend([leg]) #it is resetting the legend after each call.
use a legends = [];
legends.append([leg])
after all function calls, draw the legend differently
ax.legend(legends)
My data has three categorical variables I'm trying to visualize:
City (one of five)
Occupation (one of four)
Blood type (one of four)
So far, I've succeeded in grouping the data in a way that I think will be easy to work with:
import numpy as np, pandas as pd
# Make data
cities = ['Tijuana','Las Vegas','Los Angeles','Anaheim','Atlantis']
occupations = ['Doctor','Lawyer','Engineer','Drone security officer']
bloodtypes = ['A','B','AB','O']
df = pd.DataFrame({'City': np.random.choice(cities,500),
'Occupation': np.random.choice(occupations,500),
'Blood Type':np.random.choice(bloodtypes,500)})
# You need to make a dummy column, otherwise the groupby returns an empty df
df['Dummy'] = np.ones(500)
# This is now what I'd like to plot
df.groupby(by=['City','Occupation','Blood Type']).count().unstack(level=1)
Returns:
Dummy
Occupation Doctor Drone security officer Engineer Lawyer
City Blood Type
Anaheim A 7 7 7 7
AB 6 10 8 5
B 2 10 4 2
O 4 3 3 6
Atlantis A 6 5 5 7
AB 12 7 7 10
B 7 4 7 3
O 7 4 6 4
Las Vegas A 8 4 8 5
AB 5 6 8 9
B 6 10 6 6
O 6 9 5 9
Los Angeles A 7 4 8 8
AB 9 8 8 8
B 3 6 4 1
O 9 11 11 9
Tijuana A 3 4 5 3
AB 9 5 5 7
B 3 6 4 9
O 3 5 5 8
My goal is to create something like the Seaborn swarmplot shown below, which comes from the Seaborn documentation. Seaborn applies jitter to the quantitative data so that you can see the individual data points and their hues:
With my data, I'd like to plot City on the x-axis and Occupation on the y-axis, applying jitter to each, and then hue by Blood type. However, sns.swarmplot requires one of the axes to be quantitative:
sns.swarmplot(data=df,x='City',y='Occupation',hue='Blood Type')
returns an error.
An acceptable alternative might be to create 20 categorical bar plots, one for each intersection of City and Occupation, which I would do by running a for loop over each category, but I can't imagine how I'd feed that to matplotlib subplots to get them in a 4x5 grid.
The most similar question I could find was in R, and the asker only wanted to indicate the most common value for the third variable, so I didn't get any good ideas from there.
Thanks for any help you can provide.
Alright, I got to work on the "acceptable alternative" today and I have found a solution using basically pure matplotlib (but I stuck the Seaborn styling on top of it, just because).
import numpy as np, pandas as pd
import matplotlib.pyplot as plt
from matplotlib.cm import get_cmap
from matplotlib.patches import Patch
import seaborn as sns
# Make data
cities = ['Tijuana','Las Vegas','Los Angeles','Anaheim','Atlantis']
occupations = ['Doctor','Lawyer','Engineer','Drone security officer']
bloodtypes = ['A','B','AB','O']
df = pd.DataFrame({'City': np.random.choice(cities,500),
'Occupation': np.random.choice(occupations,500),
'Blood Type':np.random.choice(bloodtypes,500)})
# Make a dummy column, otherwise the groupby returns an empty df
df['Dummy'] = np.ones(500)
# This is now what I'd like to plot
grouped = df.groupby(by=['City','Occupation','Blood Type']).count().unstack()
# List of blood types, to use later as categories in subplots
kinds = grouped.columns.levels[1]
# colors for bar graph
colors = [get_cmap('viridis')(v) for v in np.linspace(0,1,len(kinds))]
sns.set(context="talk")
nxplots = len(grouped.index.levels[0])
nyplots = len(grouped.index.levels[1])
fig, axes = plt.subplots(nxplots,
nyplots,
sharey=True,
sharex=True,
figsize=(10,12))
fig.suptitle('City, occupation, and blood type')
# plot the data
for a, b in enumerate(grouped.index.levels[0]):
for i, j in enumerate(grouped.index.levels[1]):
axes[a,i].bar(kinds,grouped.loc[b,j],color=colors)
axes[a,i].xaxis.set_ticks([])
axeslabels = fig.add_subplot(111, frameon=False)
plt.tick_params(labelcolor='none', top=False, bottom=False, left=False, right=False)
plt.grid(False)
axeslabels.set_ylabel('City',rotation='horizontal',y=1,weight="bold")
axeslabels.set_xlabel('Occupation',weight="bold")
# x- and y-axis labels
for i, j in enumerate(grouped.index.levels[1]):
axes[nyplots,i].set_xlabel(j)
for i, j in enumerate(grouped.index.levels[0]):
axes[i,0].set_ylabel(j)
# Tune this manually to make room for the legend
fig.subplots_adjust(right=0.82)
fig.legend([Patch(facecolor = i) for i in colors],
kinds,
title="Blood type",
loc="center right")
Returns this:
I'd appreciate any feedback, and I'd still love it if someone could provide the preferred solution.
I have two different data frames in following format.
dfclean
Out[1]:
obj
0 682
1 101
2 33
dfmalicious
Out[2]:
obj
0 17
1 43
2 8
3 9
4 211
My use-case is to plot a single scatter graph that distinctly shows the obj values from both the dataframes. I am using python for this purpose. I looked at a few examples where two columns of same dataframe were used to plot the data but couldnt replicate it for my use-case. Any help is greatly appreciated.
How to plot two DataFrame on same graph for comparison
To plot multiple column groups in a single axes, repeat plot method specifying target ax
Option 1]
In [2391]: ax = dfclean.reset_index().plot(kind='scatter', x='index', y='obj',
color='Red', label='G1')
In [2392]: dfmalicious.reset_index().plot(kind='scatter', x='index', y='obj',
color='Blue', label='G2', ax=ax)
Out[2392]: <matplotlib.axes._subplots.AxesSubplot at 0x2284e7b8>
Option 2]
In [2399]: dff = dfmalicious.merge(dfclean, right_index=True, left_index=True,
how='outer').reset_index()
In [2406]: dff
Out[2406]:
index obj_x obj_y
0 0 17 682.0
1 1 43 101.0
2 2 8 33.0
3 3 9 NaN
4 4 211 NaN
In [2400]: ax = dff.plot(kind='scatter', x='index', y='obj_x', color='Red', label='G1')
In [2401]: dff.plot(kind='scatter', x='index', y='obj_y', color='Blue', label='G2', ax=ax)
Out[2401]: <matplotlib.axes._subplots.AxesSubplot at 0x11dbe1d0>
CH Gayle 17
YK Pathan 16
AB de Villiers 15
DA Warner 14
SK Raina 13
RG Sharma 13
MEK Hussey 12
AM Rahane 12
MS Dhoni 12
G Gambhir 12
I have a series like this. I want to plot the player on the x axis and their respective value on the y axis. I tried this code:
man_of_match=(matches['player_of_match'].value_counts())
sns.countplot(x=(man_of_match),data=matches,color='B')
sns.plt.show()
But with this code, it plots the frequency of the numeric value, i.e on x axis 12 gets plotted and the count on y axis becomes 4. Similarly for 13 on x axis it shows 2 on y axis.
How do i make the x axis show the name of the player and the y axis the corresponding value of the player.?
sns.countplot is meant to do the counting for you. You are counting yourself with value_counts then plotting the counts of counts. Pass matches directly to sns.countplot
ax = sns.countplot(matches['player_of_match'], color='B')
plt.sca(ax)
plt.xticks(rotation=90);
If you want to limit it to the top 10 players. Use value_counts as you did. But use matplotlib directly, to plot.
ax = matches['player_of_match'].value_counts().head(10).plot.bar(width=.8, color='R')
ax.set_xlabel('player_of_match')
ax.set_ylabel('count')
You can get it to look a lot like the seaborn plot
kws = dict(width=.8, color=sns.color_palette('pastel'))
ax = matches['player_of_match'].value_counts().head(10).plot.bar(**kws)
ax.set_xlabel('player_of_match')
ax.set_ylabel('count')
ax.grid(False, axis='x')
I have two sets of data I want to plot together on a single figure. I have a set of flow data at 15 minute intervals I want to plot as a line plot, and a set of precipitation data at hourly intervals, which I am resampling to a daily time step and plotting as a bar plot. Here is what the format of the data looks like:
2016-06-01 00:00:00 56.8
2016-06-01 00:15:00 52.1
2016-06-01 00:30:00 44.0
2016-06-01 00:45:00 43.6
2016-06-01 01:00:00 34.3
At first I set this up as two subplots, with precipitation and flow rate on different axis. This works totally fine. Here's my code:
import matplotlib.pyplot as plt
import pandas as pd
from datetime import datetime
filename = 'manhole_B.csv'
plotname = 'SSMH-2A B'
plt.style.use('bmh')
# Read csv with precipitation data, change index to datetime object
pdf = pd.read_csv('precip.csv', delimiter=',', header=None, index_col=0)
pdf.columns = ['Precipitation[in]']
pdf.index.name = ''
pdf.index = pd.to_datetime(pdf.index)
pdf = pdf.resample('D').sum()
print(pdf.head())
# Read csv with flow data, change index to datetime object
qdf = pd.read_csv(filename, delimiter=',', header=None, index_col=0)
qdf.columns = ['Flow rate [gpm]']
qdf.index.name = ''
qdf.index = pd.to_datetime(qdf.index)
# Plot
f, ax = plt.subplots(2)
qdf.plot(ax=ax[1], rot=30)
pdf.plot(ax=ax[0], kind='bar', color='r', rot=30, width=1)
ax[0].get_xaxis().set_ticks([])
ax[1].set_ylabel('Flow Rate [gpm]')
ax[0].set_ylabel('Precipitation [in]')
ax[0].set_title(plotname)
f.set_facecolor('white')
f.tight_layout()
plt.show()
2 Axis Plot
However, I decided I want to show everything on a single axis, so I modified my code to put precipitation on a secondary axis. Now my flow data data has disppeared from the plot, and even when I set the axis ticks to an empty set, I get these 00:15 00:30 and 00:45 tick marks along the x-axis.
Secondary-y axis plots
Any ideas why this might be occuring?
Here is my code for the single axis plot:
f, ax = plt.subplots()
qdf.plot(ax=ax, rot=30)
pdf.plot(ax=ax, kind='bar', color='r', rot=30, secondary_y=True)
ax.get_xaxis().set_ticks([])
Here is an example:
Setup
In [1]: from matplotlib import pyplot as plt
import pandas as pd
import numpy as np
%matplotlib inline
df = pd.DataFrame({'x' : np.arange(10),
'y1' : np.random.rand(10,),
'y2' : np.square(np.arange(10))})
df
Out[1]: x y1 y2
0 0 0.451314 0
1 1 0.321124 1
2 2 0.050852 4
3 3 0.731084 9
4 4 0.689950 16
5 5 0.581768 25
6 6 0.962147 36
7 7 0.743512 49
8 8 0.993304 64
9 9 0.666703 81
Plot
In [2]: fig, ax1 = plt.subplots()
ax1.plot(df['x'], df['y1'], 'b-')
ax1.set_xlabel('Series')
ax1.set_ylabel('Random', color='b')
for tl in ax1.get_yticklabels():
tl.set_color('b')
ax2 = ax1.twinx() # Note twinx, not twiny. I was wrong when I commented on your question.
ax2.plot(df['x'], df['y2'], 'ro')
ax2.set_ylabel('Square', color='r')
for tl in ax2.get_yticklabels():
tl.set_color('r')
Out[2]: