I am trying to put a y-axis on the right and left side of a graph. I am using pandas where I have a data frame take a certain range in an excel sheet and graph it out. The code is able to plot out the three columns that I want vs y however I'm confused on how to get the PM3 scatter plot (ax2) on the right side while keeping the PM1 and AFS scatter plot (ax1 and ax3) on the left. I tried using twinx() and other commands but it doesn't work how I want it. Any suggestions?
import pandas as pd
import matplotlib.pyplot as plt
import numpy as np
testproject = r"C:\Users\223070186\Documents\PleaseWork.xlsx"
var = pd.read_excel(testproject, sheet_name ="Test1")
df = pd.DataFrame(var, columns = ["Time", "PM1", "PM3", "AFS"])
df2 = df.iloc[1108:1142, 0:4]
ax1 = df2.plot(kind = "scatter", x = "Time", y = "PM1", color = "r")
ax2 = df2.plot(kind = "scatter", x="Time", y = "PM3", color = "purple", ax =ax1)
ax3 = df2.plot(kind = "scatter", x = "Time", y= "AFS", color = "orange", ax = ax2)
plt.xlabel("Time")
plt.ylabel("PM1, PM3, AFS")
plt.title("Time vs PM1, PM3, AFS splits")
plt.show(ax1 == ax2 == ax3)
Related
When I change the legend titles, only the first of my two markers is redrawn, am I doing something wrong, or is there a workaround?
I ran the code below:
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
#create dataframe
np.random.seed(seed=10)
x = np.arange(0,20)
y1 = np.arange(0,20) + 10*np.random.rand(20)
y2 = np.arange(20,0,-1) + 10*np.random.rand(20)
mask = np.random.randint(low=0, high=2, size=20, dtype=bool)
df = pd.DataFrame()
df['x'] = x
df['y1'] = y1
df['y2'] = y2
df['mask'] = mask
#plot results
fig, axs = plt.subplots(2)
f1 = sns.scatterplot(data=df, y='y1', x='x', ax=axs[0], hue="mask")
f2 = sns.scatterplot(data=df, y='y2', x='x', ax=axs[1], hue="mask")
new_title = ""
new_labels = ['Include', 'Exclude']
for ax in axs:
ax.legend(title=new_title, labels=new_labels)
fig.savefig(r"C:\\Temp\error.png", bbox="tight")
The result is below. As you can see the "Include" label in the legend has a marker, but the "Exclude" label does not
I am trying to merge(subplot) two combo-chart with the following code .
The problem is there is no ( or wrong ) lineplot for the first subplot (ax1 & ax2 ).
the left lineplot should look like the right one ( not equal ) .
row data is OK for all subplots , since when i plot them stand alone it works just fine.
what am i doing wrong here.
thanks
'''
#Create combo chart
fig = plt.figure(figsize=(15,10))
gs = fig.add_gridspec(1, 2, hspace=0, wspace=0)
(ax1, ax3) = gs.subplots(sharex='col', sharey='row')
###########################################
fig.autofmt_xdate(rotation=25)
ax1 = sns.barplot(x='STRT', y='Qnt', data = utw, ax = ax1,palette='summer',ci=None)
ax2 = plt.twinx()
ax2 = sns.lineplot(x='STRT', y='Yield', hue='Yield_m', data = utw,ax =ax1, sort=False, color=color,ci=None)
###########################################
ax3 = sns.barplot(x='STRT', y='Qnt', data = jbl, ax = ax3,palette='summer',ci=None)
#ax2=ax.append(ax[0].twinx())
ax4 = plt.twinx()
ax4 = sns.lineplot(x='STRT', y='Yield', hue='Yield_m', data = jbl,ax=ax4, sort=False, color=color,ci=None)
###########################
'''
Could someone give me a tip on how to do multiple Y axis plots?
This is some made up data below, how could I put Temperature its own Y axis, Pressure on its own Y axis, and then have both Value1 and Value2 on the same Y axis. I am trying to go for the same look and feel of this SO post answer. Thanks for any tips, I don't understand ax3 = ax.twinx() process, like as far as do I need to define an ax.twinx() for each separate Y axis plot I need?
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
rows,cols = 8760,4
data = np.random.rand(rows,cols)
tidx = pd.date_range('2019-01-01', periods=rows, freq='H')
df = pd.DataFrame(data, columns=['Temperature','Value1','Pressure','Value2'], index=tidx)
# using subplots() function
fig, ax = plt.subplots(figsize=(25,8))
plt.title('Multy Y Plot')
ax2 = ax.twinx()
ax3 = ax.twinx()
ax4 = ax.twinx()
plot1, = ax.plot(df.index, df.Temperature)
plot2, = ax2.plot(df.index, df.Value1, color = 'r')
plot3, = ax3.plot(df.index, df.Pressure, color = 'g')
plot4, = ax4.plot(df.index, df.Value2, color = 'b')
ax.set_xlabel('Date')
ax.set_ylabel('Temperature')
ax2.set_ylabel('Value1')
ax3.set_ylabel('Pressure')
ax4.set_ylabel('Value2')
plt.legend([plot1,plot2,plot3,plot4],list(df.columns))
# defining display layout
plt.tight_layout()
# show plot
plt.show()
This will output everything jumbled up on the same side without separate Y axis for Pressure, Value1, and Value2.
You are adding 4 different plots in one, which is not helpful. I would recommend breaking it into 2 plots w/ shared x-axis "Date":
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
rows,cols = 8760,4
data = np.random.rand(rows,cols)
tidx = pd.date_range('2019-01-01', periods=rows, freq='H')
df = pd.DataFrame(data, columns=['Temperature','Value1','Pressure','Value2'], index=tidx)
fig, (ax1, ax2) = plt.subplots(2, 1, figsize=(25,8))
plt.title('Multy Y Plot')
ax1b = ax1.twinx()
plot1a, = ax1.plot(df.index, df.Temperature)
plot1b, = ax1b.plot(df.index, df.Pressure, color='r')
ax1.set_ylabel('Temperature')
ax1b.set_ylabel('Pressure')
ax2b = ax2.twinx()
plot2a, = ax2.plot(df.index, df.Value1, color='k')
plot2b, = ax2b.plot(df.index, df.Value2, color='g')
ax2.set_xlabel('Date')
ax2.set_ylabel('Value1')
ax2b.set_ylabel('Value2')
plt.legend([plot1a, plot1b, plot2a, plot2b], df.columns)
# defining display layout
plt.tight_layout()
# show plot
plt.show()
Here I have added in the first plot (on the top) Temperature and Pressure and on the second plot (on the bottom) Value 1 and Value 2. Normally, we add in the same plot things that make sense to compare on the same x-axis. Pressure and Temperature is a valid combination that is why I combined those two together. But you can do as you wish.
This answer below uses mpatches is how to make the subplot of Value1 and Value2 on the same axis. The solution for this post has subplot for Value1 and Value2 on different axis. Thanks for the help #tzinie!
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import matplotlib.patches as mpatches
rows,cols = 8760,4
data = np.random.rand(rows,cols)
tidx = pd.date_range('2019-01-01', periods=rows, freq='H')
df = pd.DataFrame(data, columns=['Temperature','Value1','Pressure','Value2'], index=tidx)
fig, (ax1, ax2) = plt.subplots(2, 1, figsize=(25,8))
plt.title('Multy Y Plot')
ax1b = ax1.twinx()
plot1a, = ax1.plot(df.index, df.Temperature, color='r') # red
plot1b, = ax1b.plot(df.index, df.Pressure, color='b') # blue
ax1.set_ylabel('Temperature')
ax1b.set_ylabel('Pressure')
ax2.plot(df.index, df.Value1, color='k') # black
ax2.plot(df.index, df.Value2, color='g') # green
ax2.set_xlabel('Date')
ax2.set_ylabel('Value1 & Value2')
red_patch = mpatches.Patch(color='red', label='Temperature')
blue_patch = mpatches.Patch(color='blue', label='Pressure')
green_patch = mpatches.Patch(color='green', label='Value2')
black_patch = mpatches.Patch(color='black', label='Value1')
plt.legend(handles=[red_patch,blue_patch,green_patch,black_patch])
# defining display layout
#plt.tight_layout()
# show plot
plt.show()
I have data where I have names, proportions and total. I want to show all 3 variables in one plot. Ideally I want to have everything like plot 1 but inside I want to show total as in plot 2
In first plot I don't get line right also this is not my plot of choice.
import pandas as pd
from matplotlib import pyplot as plt
import seaborn as sns
df = pd.DataFrame({"name": list("ABCDEFGHIJ"), "proportion": [0.747223, 0.785883, 0.735542, 0.817368, 0.565193, 0.723029, 0.723004, 0.722595, 0.783929, 0.55152],
"total": [694327, 309681, 239384, 201646, 192267, 189399, 181974, 163483, 157902, 153610]})
fig, ax1 = plt.subplots()
ax2 = ax1.twinx()
sns.barplot(data=df, x="name", y="total", color="lightblue", ax=ax1)
sns.lineplot(data=df, x="name", y= "proportion", color="black", lw=3, ls="--", ax=ax2)
# Plot the figure.
df["male"] = df.proportion * df.total
ax = sns.barplot(data = df, x= "name", y = 'total', color = "lightblue")
sns.barplot(data = df, x="name", y = "male", color = "blue", ax = ax)
ax.set_ylabel("male/no_of_streams")
Is there a way I can achieve my goal of effective plot where
I can show total split
I also want to add proportions values to plot as well
Any help would be appreciated
Thanks in advance
If my understanding is right, for the first plot, I guess you wanna to know why the line is dashed. Just remove argument ls="--", you will get solid line.
The second, following code can work, if you want percentage of "man-number" / "total". If the percentage is computed using other numbers, you can adjust the equation in the for statement:
import pandas as pd
from matplotlib import pyplot as plt
import seaborn as sns
if __name__ == '__main__':
df = pd.DataFrame({"name": list("ABCDEFGHIJ"), "proportion": [0.747223, 0.785883, 0.735542, 0.817368, 0.565193, 0.723029, 0.723004, 0.722595, 0.783929, 0.55152], "total": [694327, 309681, 239384, 201646, 192267, 189399, 181974, 163483, 157902, 153610]})
# fig, ax1 = plt.subplots()
# ax2 = ax1.twinx()
# sns.barplot(data=df, x="name", y="total", color="lightblue", ax=ax1)
# # remove ls='--'
# sns.lineplot(data=df, x="name", y="proportion", color="black", lw=3, ax=ax2)
# Plot the figure.
df["male"] = df.proportion * df.total
ax = sns.barplot(data = df, x= "name", y = 'total', color = "lightblue")
sns.barplot(data = df, x="name", y = "male", color = "blue", ax = ax)
ax.set_ylabel("proportion(male/no_of_streams)")
# this is code block to add percentage
for i, v in enumerate(df['proportion']):
p = ax.patches[i]
height = p.get_height()
ax.text(p.get_x()+p.get_width()/2.,
height + 3,
'{:1.0f}%'.format(v * 100),
ha="center")
plt.show()
BTW, I learn at this page, FYI.
I have the following script for generating a figure with two subplots: one line plot, and one bar plot.
import matplotlib.pyplot as plt
import pandas as pd
import numpy as np
plt.close('all')
np.random.seed(42)
n = 1000
idx = pd.date_range(end='2020-02-27', periods=n)
df = pd.Series(np.random.randint(-5, 5, n),
index=idx)
curve = df.cumsum()
bars = df.resample('M').sum()
fig = plt.figure()
ax1 = fig.add_subplot(211)
ax2 = fig.add_subplot(212)
curve.plot(ax=ax1)
bars.plot(kind='bar', ax=ax2)
fig.set_tight_layout(True)
I would like to share the x axis between the two subplots, however the command ax2 = fig.add_subplot(212, sharex=ax1) will result in an empty graph for the line plot like the following figure.
Here is my version based on Matplotlib (without pandas api for plotting), may be it would be helpful.
I explicitly set the width of bars.
import matplotlib.pyplot as plt
import pandas as pd
import numpy as np
%matplotlib inline
plt.close('all')
np.random.seed(42)
n = 1000
idx = pd.date_range(end='2020-02-27', periods=n)
df = pd.Series(np.random.randint(-5, 5, n), index=idx)
curve = df.cumsum()
bars = df.resample('M').sum()
#fig = plt.figure()
#ax1 = fig.add_subplot(211)
#ax2 = fig.add_subplot(212)
#curve.plot(ax=ax1)
#bars.plot(kind='bar', ax=ax2)
fig, (ax1, ax2) = plt.subplots(2, 1, sharex=True, gridspec_kw={'hspace': 0})
ax1.plot(curve.index, curve.values)
ax2.bar(bars.index, bars.values, width = (bars.index[0] - bars.index[1])/2)
fig.set_tight_layout(True)
_ = plt.xticks(bars.index, bars.index, rotation=90)