>>> s = pd.Series(['lower', 'CAPITALS', 'this is a sentence', 'SwApCaSe']) >>> s.str.title() 0 Lower 1 Capitals 2 This Is A Sentence 3 Swapcase dtype: object
Here is what the above code is Doing:
1. We create a Series object with four strings.
2. We call the str attribute of the Series object and then call the title method.
3. The title method returns a new Series object with the same index as the original Series object.
import numpy as np a = np.random.rand(3,2) # Normalised [0,1] b = (a – np.min(a))/np.ptp(a) # Normalised [0,255] as integer: don’t forget the parenthesis before astype(int) c = (255*(a – np.min(a))/np.ptp(a)).astype(int) # Normalised [-1,1] d = 2.*(a – np.min(a))/np.ptp(a)-1 Here is what the above code is Doing: 1. np.ptp(a) is the range of a, i.e….
>>> import datetime >>> your_timestamp = 1331856000000 >>> date = datetime.datetime.fromtimestamp(your_timestamp / 1e3) >>> date datetime.datetime(2012, 3, 16, 1, 0) Here is what the above code is Doing: 1. The first line imports the datetime module. 2. The second line creates a variable called your_timestamp and assigns it the value 1331856000000. 3. The third line…
driver.switch_to_frame(“frameName”) Here is what the above code is Doing: 1. The driver.get method will navigate to a page given by the URL. WebDriver will wait until the page has fully loaded (that is, the “onload” event has fired) before returning control to your test or script. It’s worth noting that if your page uses a…
your_list = [“apple”, “orange”, “grapes”] last_value = your_list[-1] Here is what the above code is Doing: 1. We create a list called your_list. 2. We create a variable called last_value and set it equal to the last element in your_list. 3. We print the value of last_value.
pd.concat([df1, df2], axis=1) Here is what the above code is Doing: 1. We’re creating two DataFrames, df1 and df2. 2. We’re concatenating them along the columns (axis=1) and assigning the result to df. 3. We’re printing the first five rows of df. Note that the index values of the two DataFrames are preserved.
import numpy as np x = np.array([1,1,1,2,2,2,5,25,1,1]) unique, counts = np.unique(x, return_counts=True) print np.asarray((unique, counts)).T Here is what the above code is Doing: 1. np.unique(x) returns a sorted array of unique elements in x. 2. np.unique(x, return_counts=True) returns a sorted array of unique elements in x and the number of times each element appears in…