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get certain columns pandas with string
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import pandas as pd data = {'spike-2': [1,2,3], 'hey spke': [4,5,6], 'spiked-in': [7,8,9], 'no': [10,11,12]} df = pd.DataFrame(data) spike_cols = [col for col in df.columns if 'spike' in col] print(list(df.columns)) print(spike_cols)
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Rescale each feature individually to a common range [min, max] linearly using column summary statistics, which is also known as min-max normalization or Rescaling
Return a new RDD by applying a function to each element of this RDD.
Return a new RDD containing only the elements that satisfy a predicate.
Return a new RDD containing the distinct elements in this RDD.
Return an RDD containing all pairs of elements with matching keys in self and other.
Return an RDD created by coalescing all elements within each partition into a list.
Return an RDD of grouped items.
Return an RDD with the keys of each tuple.
Return an RDD with the values of each tuple
Return the Cartesian product of this RDD and another one, that is, the RDD of all pairs of elements (a, b) where a is in self and b is in other.
Return the intersection of this RDD and another one
Return the key-value pairs in this RDD to the master as a dictionary.
Return the number of elements in this RDD.
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Save this RDD as a SequenceFile of serialized objects
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Write a function called square_odd that has one parameter. Your function must calculate the square of each odd number in a list. Return a Python 3 list containing the squared values Challenge yourself: Solve this problem with a list comprehension!
Write a function isRed() that accepts a string parameter and looks for the presence of the word ‘red’ in the string. If it is found, return boolean True otherwise False. Finally output the result of calling the function with the value in text.
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You will be provided a file path for input I, a file path for output O, a string S, and a string T. Read the contents of I, replacing each occurrence of S with T and write the resulting information to file O. You should replace O if it already exists.
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