TypeError: an integer is required (got type Timestamp)

This error is common when a date is extracted from a pandas dataframe datetime column or index. We need to extract the date from the Timestamp object by using the .date() method:

startdate
Timestamp('2019-10-28 05:00:00')

startdate.date()
datetime.date(2019, 10, 28)

If we need to convert to a datetime format then we need to combine the date and the time portion like this

startdate = dt.datetime.combine(startdate.date(), startdate.time())

dt.datetime.combine() combines the date and the time part together to form a datetime object.

To search the python interpreter history we can use the following code as it is with the string search_string replaced by string to be searched.

>>> import readline
>>> for i in range(readline.get_current_history_length()):
…       x=readline.get_history_item(i+1)
…       if “search_string” in x:
…          print(x)
…

Note: The indentation of the code is crucial to its function.

Matplotlib fill function can fill a polygon given the coordinates of its vertices. See the first example in the following link.

https://matplotlib.org/gallery/lines_bars_and_markers/fill.html

This method can be extended to draw filled curves or shade the area under a curve. For example, if we want to shade the area under a normal distribution up to the z-score -1.4 like this

then we will first generate a list of (x,y) coordinate pairs along the curve of the distribution from x=-4 to x=-1.4 using y = Exp(-x^2/2)/√ (2π).

y-values are generated using the scipy function norm() which returns the value of the Gaussian Exp(-x^2/2)/√ (2π). This will generate (x,y) pairs along the curve of the Gaussian. However, we will need to add the (-1.4,0) coordinate so that the curve closes. 

from scipy.stats import norm
import matplotlib.pyplot as plt
import numpy as np

interval = 0.1

x = np.arange(-4, 4, interval)
y = norm.pdf(x)

# z-score
z = -1.4

# we want to shade from -inf to z or rather the interval [-x_min, z]
# generate x-coordinates: [-x_min,z]
xarea = np.arange(-4, z, interval) 
# generate y-coordinates
yarea = norm.pdf(xarea)  
# need to add the point (x,y): (z, 0) so that 
# polygon fills all the way to the x-axis
# append x=z-score
xarea = np.append(xarea, xarea[-1]) 
# append y=0
yarea = np.append(yarea, 0 )

plt.plot(x, y)

plt.fill(xarea, yarea)

plt.text(-1.4 , 0 , '-1.4' , {'color':'r'})

plt.show()

Maximize plots from within code

# show plot in a maximized window
mng = plt.get_current_fig_manager()
mng.full_screen_toggle()

References
https://matplotlib.org/gallery/text_labels_and_annotations/usetex_demo.html#sphx-glr-gallery-text-labels-and-annotations-usetex-demo-py

Every column of a dataframe can be plotted provided they contain numeric values. To demonstrate this we will generate a dataframe from S&P data pulled from Yahoo finance using pandas_datareader. Then we plot one of the columns of the dataframe – the column labeled ‘Adj Close’ (adjusted close). We will use pyplotlib to do the plotting but seaborn can also be used.

from matplotlib import pyplot as plt
import datetime
# pandas_datareader imports stock data from yahoo finance 
from pandas_datareader import data as web

start = datetime.datetime(2018,1,5)
end = datetime.datetime(2018,2,5)
# get some data from yahoo finance
spdrdata = web.DataReader('SPY',"yahoo",start,end)
# plot the column of the dataframe called 'Adj Close'
spdrdata['Adj Close'].plot()
# display with pyplot
plt.show()

spdrdata[‘Adj Close’].plot() selects the column and directly plots it. The plot() function automatically selects the index of the dataframe as the x-axis. The index for this particular dataframe are the dates. 

>>> spdrdata.index
DatetimeIndex(['2018-01-05', '2018-01-08', '2018-01-09', '2018-01-10',
               '2018-01-11', '2018-01-12', '2018-01-16', '2018-01-17',
               '2018-01-18', '2018-01-19', '2018-01-22', '2018-01-23',
               '2018-01-24', '2018-01-25', '2018-01-26', '2018-01-29',
               '2018-01-30', '2018-01-31', '2018-02-01', '2018-02-02',
               '2018-02-05', '2018-02-06'],
              dtype='datetime64[ns]', name='Date', freq=None)