Python Loop iteration with replacement of null values in timeseries of a list

pandas replace
pandas replace values in column based on condition
pandas replace specific values in column
pandas apply
pandas groupby
a value is trying to be set on a copy of a slice from a dataframe
pandas loc
fillna pandas

I need to replace null values with value of the list below in Python. Value will be same for any item through out the list if its not null.

example of list

[[1538140080000, None], [1538140140000, None], [1538140200000, None], [1538140260000, None], [1538140320000, None], [1538140380000, 92881926.0], [1538140440000, 92881926.0]]

I would like to get the output to be

[[1538140080000, 92881926.0], [1538140140000, 92881926.0], [1538140200000, 92881926.0], [1538140260000, 92881926.0], [1538140320000, 92881926.0], [1538140380000, 92881926.0], [1538140440000, 92881926.0]]

In the list any one of the (key, value) will have the required value (apart from null), which I want to replace with for all the keys of the list.


a list comprehension can easily do the job

l = [[q if q else 0 for q in i] for i in l]

Output

[[1538140080000, 0], [1538140140000, 0], [1538140200000, 0], [1538140260000, 0], [1538140320000, 0], [1538140380000, 92881926.0], [1538140440000, 92881926.0]]

updated

var = [[i[q] for i in l if i[q]] for q in range(len(l[0]))]
l  = [[q if q else var[w][0] for w,q in enumerate(i)] for i in l]

output

[[1538140080000, 92881926.0], [1538140140000, 92881926.0], [1538140200000, 92881926.0], [1538140260000, 92881926.0], [1538140320000, 92881926.0], [1538140380000, 92881926.0], [1538140440000, 92881926.0]]

Pandas Tutorial: Replacing Values in , Matplotlib Tutorial: replace, at, loc to change values. We will measure the time behaviour in the following code examples: With replace it is possible to replace values in a Series or DataFrame without knowing where they occur. Second, if regex=True then all of the strings in both lists will be interpreted as regexs  It is roughly equivalent to i += 1 in Python. This loop is interpreted as follows: Initialize i to 1. Continue looping as long as i <= 10. Increment i by 1 after each loop iteration. Three-expression for loops are popular because the expressions specified for the three parts can be nearly anything,


Could be a one-liner, if you hard-code replacement.

Also, if you can allow lazy evaluation, you can eliminate the list(...) call.

replacement = 0
l = list( map(lambda (a,b): [a,replacement if b == None else b], l) )

Working with Missing Data in Pandas, None: None is a Python singleton object that is often used for missing data in Python code. there are several useful functions for detecting, removing, and replacing null values in Pandas DataFrame : These function can also be used in Pandas Series in order to find null values in a series. creating a dataframe from list. Python is a great language for doing data analysis, primarily because of the fantastic ecosystem of data-centric Python packages. Pandas is one of those packages, and makes importing and analyzing data much easier. Sometimes csv file has null values, which are later displayed as NaN in Data Frame. Just like pandas dropna () method manage and


l = [[1538140080000, None], [1538140140000, None], [1538140200000, None], [1538140260000, None], [1538140320000, None], [1538140380000, 92881926.0], [1538140440000, 92881926.0]]

replacement = next(filter(None, list(zip(*l))[1]))

for sl in l:
     for i, x in enumerate(sl):
         if x is None:
             sl[i] = replacement
print(l)

outputs

[[1538140080000, 92881926.0],
 [1538140140000, 92881926.0],
 [1538140200000, 92881926.0],
 [1538140260000, 92881926.0],
 [1538140320000, 92881926.0],
 [1538140380000, 92881926.0],
 [1538140440000, 92881926.0]]

Optimum approach for iterating over a DataFrame, In this session I am going to be talking about iterating over rows in a going to examine different ways in which we can replace null values in That is more than 7 times faster than using iterrows. The apply() method can be called on a single Pandas Series (as I Navigating The Hell of NaNs in Python. Iterate over the list using while loop. Fist get the size of list. Then iterate using while loop from 0 to len (list) – 1. In each iteration access iTh element. ''' Iterate over the list using while loop ''' i = 0 sizeofList = len (wordList) while i < sizeofList : print (wordList [i]) i += 1. Iterate over the list using while loop.


If you are happy to use a 3rd party library, Pandas offers vectorised methods:

import pandas as pd

L = [[1538140080000, None], [1538140140000, None], [1538140200000, None],
     [1538140260000, None], [1538140320000, None], [1538140380000, 92881926.0],
     [1538140440000, 92881926.0]]

res = pd.DataFrame(L).bfill().ffill().values.tolist()

print(res)

[[1538140080000.0, 92881926.0], [1538140140000.0, 92881926.0], [1538140200000.0, 92881926.0],
 [1538140260000.0, 92881926.0], [1538140320000.0, 92881926.0], [1538140380000.0, 92881926.0],
 [1538140440000.0, 92881926.0]]

Pandas for time series data, Pandas for time series data — tricks and tips. Adrian G Re-index a dataframe to interpolate missing values (eg every 30 mins below). You need to Sort dataframe using a list (sorts by column 'id' using given list order of id's) Iterate over rows Replace NaN in df or column with zeros (or value) Also tagged Python  Python dictionaries are composed of key-value pairs, so in each loop, there are two elements we need to access (the key and the value). Instead of using enumerate() like we would with lists, to loop over both keys and the corresponding values for each key-value pair we need to call the .items() method.


Grab the value that is not None, since it will only ever be one value we can use set

Redefine items in l as [i[0], *value]

value = set([y for x, y in l if y != None])
l = [[i[0], *value] for i in l]
[[1538140080000, 92881926.0], [1538140140000, 92881926.0], [1538140200000, 92881926.0], [1538140260000, 92881926.0], [1538140320000, 92881926.0], [1538140380000, 92881926.0], [1538140440000, 92881926.0]]

Cleaning Financial Time Series data with Python, I propose using a form of Machine Learning (ML) to make time series We use a loop below to iterate over all the data tables. Next we drop those columns that have null values from the data table. cols = list(umcsi) That’s 2 questions. For the first: why can’t you modify the list that way? Well actually, you kinda can. Sorta. In the first loop, each time around, Python assigns each element of the list to the name “item”.


pandas.Series.replace, list of str, regex, or numeric: First, if to_replace and value are both lists, they must be the same length. Second  In this article we will different ways to iterate over all or certain columns of a Dataframe. Let’s first create a Dataframe i.e. Contents of created dataframe empDfObj are, Dataframe class provides a member function iteritems () i.e. It yields an iterator which can can be used to iterate over all the columns of a dataframe.


Loop better: A deeper look at iteration in Python, Dive into Python's for loops to take a look at how they work under the hood and Let's take the same list of numbers and the same generator object: But unpacking dictionaries doesn't raise errors and it doesn't return key-value pairs. And if we loop over an iterator a second time, we'll get nothing back:. Use the isnull () method to detect the missing values. The output shows True when the value is missing. By adding an index into the dataset, you obtain just the entries that are missing. The example shows the following output: 0 False 1 False 2 False 3 True 4 False 5 False 6 True dtype: bool 3 NaN 6 NaN dtype: float64. Encoding missingness.


4. More Control Flow Tools, If you do need to iterate over a sequence of numbers, the built-in function range() In fact, even functions without a return statement do return a value, albeit a rather boring one. """Return a list containing the Fibonacci series up to n. parrot() # required argument missing parrot(voltage=5.0, 'dead')  The behavior of basic iteration over Pandas objects depends on the type. When iterating over a Series, it is regarded as array-like, and basic iteration produces the values. Other data structures, like DataFrame and Panel, follow the dict-like convention of iterating over the keys of the objects. Iterating a DataFrame gives column names.