I have a dummy dataset, df:
Demand WTP
0 13.0 111.3
1 443.9 152.9
2 419.6 98.2
3 295.9 625.5
4 150.2 210.4
I would like to plot this data as a step function in which the "WTP" are y-values and "Demand" are x-values.
The step curve should start with from the row with the lowest value in "WTP", and then increase gradually with the corresponding x-values from "Demand". However, I can't get the x-values to be cumulative, and instead, my plot becomes this:
I'm trying to get something that looks like this:
but instead of a proportion along the y-axis, I want the actual values from my dataset:
This is my code:
import numpy as np
import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
Demand_quantity = pd.Series([13, 443.9, 419.6, 295.9, 150.2])
Demand_WTP = [111.3, 152.9, 98.2, 625.5, 210.4]
demand_data = {'Demand':Demand_quantity, 'WTP':Demand_WTP}
Demand = pd.DataFrame(demand_data)
Demand.sort_values(by = 'WTP', axis = 0, inplace = True)
print(Demand)
# sns.ecdfplot(data = Demand_WTP, x = Demand_quantity, stat = 'count')
plt.step(Demand['Demand'], Demand['WTP'], label='pre (default)')
plt.legend(title='Parameter where:')
plt.title('plt.step(where=...)')
plt.show()
You can try:
import matplotlib.pyplot as plt
import pandas as pd
df=pd.DataFrame({"Demand":[13, 443.9, 419.6, 295.9, 150.2],"WTP":[111.3, 152.9, 98.2, 625.5, 210.4]})
df=df.sort_values(by=["Demand"])
plt.step(df.Demand,df.WTP)
But I am not really sure about what you want to do. If the x-values are the df.Demand, than the dataframe should be sorted according to this column.
If you want to cumulate the x-values, than try to use numpy.cumsum:
import matplotlib.pyplot as plt
import pandas as pd
import numpy as np
df=pd.DataFrame({"Demand":[13, 443.9, 419.6, 295.9, 150.2],"WTP":[111.3, 152.9, 98.2, 625.5, 210.4]})
df=df.sort_values(by=["WTP"])
plt.step(np.cumsum(df.Demand),df.WTP)
Related
I have a dataframe df in which the column extracted_day consists of dates ranging between 2022-05-08 to 2022-05-12. I have another column named gas_price, which consists of the price of the gas. I want to construct a joyplot such that for each date, it shows the gas_price in the y axis and has minutes_elapsed_from_start_of_day in the x axis. We may also use ridgeplot or any other plot if this doesn't work.
This is the code that I have written, but it doesn't serve my purpose.
from joypy import joyplot
import matplotlib.pyplot as plt
df['extracted_day'] = df['extracted_day'].astype(str)
joyplot(df, by = 'extracted_day', column = 'minutes_elapsed_from_start_of_day',figsize=(14,10))
plt.xlabel("Number of minutes elapsed throughout the day")
plt.show()
Create dataframe with mock data:
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
from joypy import joyplot
np.random.seed(111)
df = pd.DataFrame({
'minutes_elapsed_from_start_of_day': np.tile(np.arange(1440), 5),
'extracted_day': np.repeat(['2022-05-08', '2022-05-09', '2022-05-10','2022-05-11', '2022-05-12'], 1440),
'gas_price': abs(np.cumsum(np.random.randn(1440*5)))})
Then create the joyplot. It is important that you set kind='values', since you do not want joyplot to show KDEs (kernel density estimates, joyplot's default) but the raw gas_price values:
joyplot(df, by='extracted_day',
column='gas_price',
kind='values',
x_range=np.arange(1440),
figsize=(7,5))
The resulting joyplot looks like this (the fake gas prices are represented by the y-values of the lines):
I want to plot a tendency line on top of a data plot. This must be simple but I have not been able to figure out how to get to it.
Let us say I have the following:
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
df = pd.DataFrame(np.random.randint(0,100,size=(100, 1)), columns=list('A'))
sns.lineplot(data=df)
ax.set(xlabel="Index",
ylabel="Variable",
title="Sample")
plt.show()
The resulting plot is:
What I would like to add is a tendency line. Something like the red line in the following:
I thank you for any feedback.
A moving average is one method (my first thought, and already suggested).
Another method is to use a polynomial fit. Since you had 100 points in your original data, I picked a 10th order fit (square root of data length) in the example below. With some modification of your original code:
idx = [i for i in range(100)]
rnd = np.random.randint(0,100,size=100)
ser = pd.Series(rnd, idx)
fit = np.polyfit(idx, rnd, 10)
pf = np.poly1d(fit)
plt.plot(idx, rnd, 'b', idx, pf(idx), 'r')
This code provides a plot like this:
You can do something like this using Rolling Average:
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
data = np.random.randint(0,100,size=(100, 1))
df["rolling_avg"] = df.A.rolling(7).mean().shift(-3)
sns.lineplot(data=df)
plt.show()
You could also do a Regression plot to analyse how data can be interpolated using:
ax = sns.regplot(x=df.index, y="A",
data=df,
scatter_kws={"s": 10},
order=10,
ci=None)
How to plot the Outliers with Box plot for the below data
no,store_id,revenue,profit,state,country
0,101,779183,281257,WD,India
1,101,144829,838451,WD,India
2,101,766465,757565,AL,Japan
Code is below, code is there till converting data to standardscalar any can choose minmaxscalar. After that How to define Quartile range to define outliers
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
from sklearn.preprocessing import StandardScaler
df = pd.read_csv(r'anomaly.csv',index_col=False);
df1 = pd.get_dummies(data=df)
df2 = StandardScaler().fit_transform(df1)
Box and whisker plots display the 25th and 75th percentiles of the data by convention.
This is calculated automatically using the medians of the data you provided.
For example, for the following data:
no,store_id,revenue,profit,state,country
0,101,779183,281257,WD,India
1,101,144829,838451,WD,India
2,101,766465,757565,AL,Japan
2,101,1000000,757565,AL,Italy
You can display the boxplot as follows for the revenue column:
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
from sklearn.preprocessing import StandardScaler
df = pd.read_csv(r'anomaly.csv',index_col=False)
df1 = pd.get_dummies(data=df)
df2 = StandardScaler().fit_transform(df1)
green_diamond = dict(markerfacecolor='g', marker='D')
fig1, ax1 = plt.subplots()
ax1.set_title('Box plot')
ax1.boxplot(df['revenue'], flierprops=green_diamond)
plt.show()
The outlier is displayed:
I am unable to get regression line and the variance bounds around it while plotting seaborn.pairplot with kind=reg as shown in the examples at http://seaborn.pydata.org/generated/seaborn.pairplot.html
import pandas pd
import seaborn as sns
import numpy as np
import matplotlib as plt
# Preparing random dataFrame with two colums, viz., random x and lag-1 values
lst1 = list(np.random.rand(10000))
df = pd.DataFrame({'x1':lst1})
df['x2'] = df['x1'].shift(1)
df = df[df['x2'] > 0]
# Plotting now
pplot = sns.pairplot(df, kind="reg")
pplot.set(ylim=(min(df['x1']), max(df['x1'])))
pplot.set(xlim=(min(df['x1']), max(df['x1'])))
plt.show()
The regression line is there, you just don't see it, because it's hidden by the unnaturally high number of points in the plot.
So let's reduce the number of points and you'll see the regression as expected.
import pandas as pd
import seaborn as sns
import numpy as np
import matplotlib.pyplot as plt
# Preparing random dataFrame with two colums, viz., random x and lag-1 values
lst1 = list(np.random.rand(100))
df = pd.DataFrame({'x1':lst1})
df['x2'] = df['x1'].shift(1)
df = df[df['x2'] > 0]
# Plotting now
pplot = sns.pairplot(df, kind="reg")
plt.show()
I have data from two sensors that I want to visualize. Both sensors take only 0/1 values. How can I change the xaxis labels to show the time series and y axis should have 2 labels 0 and 1 representing the value of sensors along the time series.
import pandas as pd
import matplotlib.pyplot as plt
def drawgraph(inputFile):
df=pd.read_csv(inputFile)
fig=plt.figure()
ax=fig.add_subplot(111)
y = df[['sensor1']]
x=df.index
plt.plot(x,y)
plt.show()
You should have explained what you tried before asking a question for this to be meaningful. Anyway, below is the example.
%matplotlib inline
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
trange = pd.date_range("11:00", "21:30", freq="30min")
df = pd.DataFrame({'time':trange,'sensor1':np.round(np.random.rand(len(trange))),\
'sensor2':np.round(np.random.rand(len(trange)))})
df = df.set_index('time')
df.plot(yticks=[0,1],ylim=[-0.1,1.1],style={'sensor1':'ro','sensor2':'bx'})