I am new to analytics,python and machine learning and I am working on Time forecasting. Using the following code I am getting the value for train and test data but the graph is plotted blank.
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import statsmodels.tsa.api as ExponentialSmoothing
#Importing data
df = pd.read_csv('international-airline-passengers - Copy.csv')
#Printing head
print(df.head())
#Printing tail
print(df.tail())
df = pd.read_csv('international-airline-passengers - Copy.csv', nrows = 11856)
#Creating train and test set
#Index 10392 marks the end of October 2013
train=df[0:20]
test=df[20:]
#Aggregating the dataset at daily level
df.Timestamp = pd.to_datetime(df.Month,format='%m/%d/%Y %H:%M')
df.index = df.Timestamp
df = df.resample('D').mean()
train.Timestamp = pd.to_datetime(train.Month,format='%m/%d/%Y %H:%M')
print('1')
print(train.Timestamp)
train.index = train.Timestamp
train = train.resample('D').mean()
test.Timestamp = pd.to_datetime(test.Month,format='%m/%d/%Y %H:%M')
test.index = test.Timestamp
test = test.resample('D').mean()
train.Count.plot(figsize=(15,8), title= 'Result', fontsize=14)
test.Count.plot(figsize=(15,8), title= 'Result', fontsize=14)
plt.show()
Not able to understand the reason for getting the graph blank even when train and test data is having value.
Thanks in advance.
I think I found the issue here. The thing is you are using train.Count.plot here, while the value of "plt" is still empty.If you go through the documentation of matplotlib(link down below), you will find that you need to store some value in plt first and here since plt is empty, it is giving back empty plot.
Basically you are not plotting anything and just showing up the blank plot.
Eg: plt.subplots(values) or plt.scatter(values), or any of its function depending on requirements.Hope this helps.
https://matplotlib.org/
import holoviews as hv
import pandas as pd
import numpy as np
data=pd.read_csv("C:/Users/Nisarg.Bhatt/Documents/data.csv", engine="python")
train=data.groupby(["versionCreated"])["Polarity Score"].mean()
table=hv.Table(train)
print(table)
bar=hv.Bars(table).opts(plot=dict(width=1500))
renderer = hv.renderer('bokeh')
app = renderer.app(bar)
print(app)
from bokeh.server.server import Server
server = Server({'/': app}, port=0)
server.start()
server.show("/")
This is done by using Holoviews, it is used for visualisation purpose.If you are using for a professional application, you should definitely try this. Here the versionCreated is date and Polarity is similar to count. Try this
OR, if you want to stick to matplotlib try this:
fig, ax = plt.subplots(figsize=(16,9))
ax.plot(msft.index, msft, label='MSFT')
ax.plot(short_rolling_msft.index, short_rolling_msft, label='20 days rolling')
ax.plot(long_rolling_msft.index, long_rolling_msft, label='100 days rolling')
ax.set_xlabel('Date')
ax.set_ylabel('Adjusted closing price ($)')
ax.legend()
Also this can be used, if you want to stick with matplotlib
Related
I am new to Python and Pandas so any help is much appreciated.
I am trying to make the graph below interactive, it would also be good to be able to choose which attributes show rather than them all.
Here is what I have so far
df.set_index('Current Year').plot(rot=45)
plt.xlabel("Year",size=16)
plt.ylabel("",size=16)
plt.title("Current year time series plot", size=18)
I know that i need to import the following import plotly.graph_objects as go but no idea how to implement this with the above time series graph. Thanks
EDIT
I am getting this error when trying to enter my plotted data.
All you need is:
df.plot()
As long as you import the correct libraries and set plotly as the plotting backend for pandas like this:
import pandas as pd
pd.options.plotting.backend = "plotly"
df = pd.DataFrame({'year':['2020','2021','2022'], 'value':[1,3,2]}).set_index('year')
fig = df.plot(title = "Current year time series plot")
fig.show()
Plot:
Complete code:
import pandas as pd
pd.options.plotting.backend = "plotly"
df = pd.DataFrame({'year':['2020','2021','2022'], 'value':[1,3,2]}).set_index('year')
fig = df.plot(title = "Current year time series plot")
fig.show()
I would like to plot signals on my chart is there is a way to do it on candle stick?
I did the following and got stuck :(
!pip install yfinance
!pip install mplfinance
import yfinance as yf
import mplfinance as mpf
import numpy as np
import pandas as pd
df=yf.download('BTC-USD',start='2008-01-04',end='2021-06-3',interval='1d')
buy=np.where((df['Close'] > df['Open']) & (df['Close'].shift(1) < df['Open'].shift(1),1,0)
fig = plt.figure(figsize = (20,10))
mpf.plot(df,figsize=(20,12),type ='candle',volume=True);
# any idea how to add the signal?
import yfinance as yf
import mplfinance as mpf
import numpy as np
df = yf.download('BTC-USD', start='2008-01-04', end='2021-06-3', interval='1d').tail(50)
buy = np.where((df['Close'] > df['Open']) & (df['Close'].shift(1) < df['Open'].shift(1)), 1, np.nan) * 0.95 * df['Low']
apd = [mpf.make_addplot(buy, scatter=True, markersize=100, marker=r'$\Uparrow$', color='green')]
mpf.plot(df, type='candle', volume=True, addplot=apd)
I just added .tail() for better visualization.
Output:
You place signals on the plot using the "make additional plot" api: mpf.make_addplot(data,**kwargs). The data that you pass in to make_addplot must be the same length as your original candlestick dataframe (so that mplfinance can line it up appropriately with the candlesticks). If you do not want to plot a signal at every location you simply fill the data with nan values except where you do want to plot a signal.
The return value from ap = mpf.make_addplot() is then passed into mpf.plot(df,addplot=ap) using the addplot kwarg.
You can see many examples in this tutorial on adding your own technical studies to plots.
Take the time (maybe 10 minutes or so) to go carefully through the entire tutorial. It will be time well spent.
I'm using pandas DataFrame and matplotlib to draw three lines in the same figure. The data ought to be correct, but when I try to plot the lines, the code returns a ValueError, which is unexpected.
The detail error warning says: ValueError: view limit minimum -105920.979 is less than 1 and is an invalid Matplotlib date value. This often happens if you pass a non-datetime value to an axis that has datetime units
How to fix this error, and plot three lines in the same figure?
import pandas as pd
import datetime as dt
import pandas_datareader as web
import matplotlib.pyplot as plt
from matplotlib import style
import matplotlib.ticker as ticker
spot=pd.read_excel('https://www.eia.gov/dnav/pet/hist_xls/RWTCd.xls',sheet_name='Data 1',skiprows=2) #this is spot price data
prod=pd.read_excel('https://www.eia.gov/dnav/pet/hist_xls/WCRFPUS2w.xls',sheet_name='Data 1',skiprows=2) #this is production data
stkp=pd.read_excel('https://www.eia.gov/dnav/pet/hist_xls/WTTSTUS1w.xls',sheet_name='Data 1',skiprows=2) #this is stockpile data
fig,ax = plt.subplots()
ax.plot(spot,label='WTI Crude Oil Price')
ax.plot(prod,label='US Crude Oil Production')
ax.plot(stkp,label='US Crude Oil Stockpile')
plt.legend()
plt.show()
print(spot,prod,stkp)
I don't get an error running the code, though I have made a couple of adjustments to the import and the plot.
Update matplotlib and pandas.
If you're using Anaconda, at the Anaconda prompt, type conda update --all
Parse the 'Date' column to datetime and set it as the index.
Place the legend outside the plot
Set the yscale to 'log', because the range of numbers is large.
import pandas as pd
import matplotlib.pyplot as plt
spot=pd.read_excel('https://www.eia.gov/dnav/pet/hist_xls/RWTCd.xls', sheet_name='Data 1',skiprows=2, parse_dates=['Date'], index_col='Date') #this is spot price data
prod=pd.read_excel('https://www.eia.gov/dnav/pet/hist_xls/WCRFPUS2w.xls', sheet_name='Data 1',skiprows=2, parse_dates=['Date'], index_col='Date') #this is production data
stkp=pd.read_excel('https://www.eia.gov/dnav/pet/hist_xls/WTTSTUS1w.xls', sheet_name='Data 1',skiprows=2, parse_dates=['Date'], index_col='Date') #this is stockpile data
fig,ax = plt.subplots()
ax.plot(spot, label='WTI Crude Oil Price')
ax.plot(prod, label='US Crude Oil Production')
ax.plot(stkp, label='US Crude Oil Stockpile')
plt.legend(bbox_to_anchor=(1.05, 1), loc='upper left')
plt.yscale('log')
plt.show()
I've been trying to do it for several hours and I have a mistake every time. I want to create 3 bar plots in one graph. The y-axis is to be between 0 and 1000.
The end result should be this
Thats my code:
import matplotlib.pyplot as plt
import numpy as np
import csv
df = pd.read_csv('razemKM.csv')
dfn = pd.read_csv('razemNPM.csv')
print(df)
y=[0,1000]
a=(df["srednia"]-df["odchStand"])
a1=df["srednia"]
a2=(df["srednia"]+df["odchStand"])
plt.bar(y,a,width=0.1,color='r')
plt.bar(y,a1,width=0.1,color='g')
plt.bar(y,a2,width=0.1,color='y')
plt.show()
You can use pandas plot function:
df['Sum'] = df["srednia"]+df["odchStand"]
df['Dif'] = df["srednia"]-df["odchStand"]
df.plot.bar(y=['Diff','srednia', 'Sum'],width=0.1)
plt.show()
I am currently trying to plot the price of the 1080 graphics card against the price of bitcoin over time, but the scales of the Y axis are just way off. This is my code so far:
import pandas as pd
from datetime import date
import matplotlib.pyplot as plt
from matplotlib.pyplot import *
import numpy as np
GPUDATA = pd.read_csv("1080Prices.csv")
BCDATA = pd.read_csv("BitcoinPrice.csv")
date = pd.to_datetime(GPUDATA["Date"])
price = GPUDATA["Price_USD"]
date1 = pd.to_datetime(BCDATA["Date"])
price1 = BCDATA["Close"]
plot(date, price)
plot(date1, price1)
And that produces this:
The GPU prices, of course, are in blue and the price of bitcoin is in orange. I am fairly new to visualizations and I'm having a rough time finding anything online that could help me fix this issue. Some of the suggestions I found on here seem to deal with plotting data from a single datasource, but my data comes from 2 datasources.
One has entries of the GPU price in a given day, the other has the open, close, high, and low price of bitcoin in a given day. I am struggling to find a solution, any advice would be more than welcome! Thank you!
What you want to do is twin the X-axis, such that both plots will share the X-axis, but have separate Y-axes. That can be done in this way:
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
GPUDATA = pd.read_csv("1080Prices.csv")
BCDATA = pd.read_csv("BitcoinPrice.csv")
gpu_dates = pd.to_datetime(GPUDATA["Date"])
gpu_prices = GPUDATA["Price_USD"]
btc_dates = pd.to_datetime(BCDATA["Date"])
btc_prices = BCDATA["Close"]
fig, ax1 = plt.subplots()
ax2 = ax1.twinx() # Create a new Axes object sharing ax1's x-axis
ax1.plot(gpu_dates, gpu_prices, color='blue')
ax2.plot(btc_dates, btc_prices, color='red')
As you have not provided sample data, I am unable to show a relevant demonstration, but this should work.