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Calculating intermittent average

I have a huge dataframe with a lot of zero values. And, I want to calculate the average of the numbers between the zero values. To make it simple, the data shows for example 10 consecutive values then it renders zeros then values again. I just want to tell python to calculate the average of each patch of the data.
The pic shows an example

first of all I'm a little bit confused why you are using a DataFrame. This is more likely being stored in a pd.Series while I would suggest storing numeric data in an numpy array. Assuming that you are having a pd.Series in front of you and you are trying to calculate the moving average between two consecutive points, there are two approaches you can follow.
zero-paddding for the last integer:
assuming circularity and taking the average between the first and the last value
Here is the expected code:
import numpy as np
import pandas as pd
data_series = pd.Series([0,0,0.76231, 0.77669,0,0,0,0,0,0,0,0,0.66772, 1.37964, 2.11833, 2.29178, 0,0,0,0,0])
np_array = np.array(data_series)
#assuming zero_padding
np_array_zero_pad = np.hstack((np_array, 0))
mvavrg_zeropad = [np.mean([np_array_zero_pad[i], np_array_zero_pad[i+1]]) for i in range(len(np_array_zero_pad)-1)]
#asssuming circularity
np_array_circ_arr = np.hstack((np_array, np_array[-1]))
np_array_circ_arr = [np.mean([np_array_circ_arr[i], np_array_circ_arr[i+1]]) for i in range(len(np_array_circ_arr)-1)]

Why is the full correlation matrix not getting calculated in Python?

I have a dataset with 4 variables(Bearing 1 to Bearing 4) and 20152319 no of observations. It looks like this:
Now, I am trying to find the correlation matrix of the 4 variables. The code I use is this:
corr_mat = Data.corr(method = 'pearson')
print(corr_mat)
However in the result, I get the correlation information for only Bearing 2 to Bearing 4. Bearing 1 is nowhere to be seen. I am providing a snapshot of the result down below:
I have tried removing NULL values from each of the variables and also tried looking for missing values but nothing works. What is interesting is that, if I isolate the first two variables (Bearing 1 and Bearing 2) and then try to find the correlation matrix between them, Bearing 1 does not come up and the matrix is a 1x1 matrix with only Bearing 2
Any explanation on why this occurs and how to solve it would be appreciated.

Try to see if the first column 'Bearing 1' is numeric.
Data.dtypes # This will show the type of each column
cols = Data.columns # Saving column names to a variable
Data[cols].apply(pd.to_numeric, errors='coerce') # Converting the columns to numeric
Now apply your Calculations,
corr_mat = Data.corr(method = 'pearson')
print(corr_mat)

Dtype of first column is object, so pandas by default omit it. Solution is convert it to numeric:
Data['Bearing 1'] = Data['Bearing 1'].astype(float)
Or if some non numeric values use to_numeric with errors='coerce' for parse these values to NaNs:
Data['Bearing 1'] = pd.to_numeric(Data['Bearing 1'], errors='coerce')
If want convert all columns to numeric:
Data = Data.astype(float)
Or:
Data = Data.apply(pd.to_numeric, errors='coerce')

Replace values in pandas DataFrame

I have the following python code:
consumos=df.iloc[:,0]
df['media_movel'] = rolling_median(consumos, window=30, center=True).fillna(method='bfill').fillna(method='ffill')
desv_padrao=df.stack().std()
threshold = 1000
difference = np.abs(consumos - df['media_movel'])
corr=np.abs(df['media_movel']-desv_padrao)
df['corr']=pd.DataFrame(corr)
outlier = difference > threshold
df.mask(outlier, df['corr'], axis=1)
So, I have a dataframe containing a time series and my aim is to correct the outliers (by admiting that the difference between the reference data and the rolling median has to be greater than 1000, which is the threshold).
For that, I've created the boolean variable outlier(which is True when there is an outlier based on the previous explanation) and I am trying to replace those outliers with: (rolling mediam column - standard deviation) into a mask, but the result is the time series with NaNs. I don't know why those NaNs appear, but I need to obtain the correct data.

I think the replacement of the masked values may be failing due to a shape mismatch. Try replacing your last line with this:
df.mask(outlier, df['corr'].values.reshape(-1, 1), axis=1)
If that fails, try this:
df.iloc[:,0].mask(outlier, df['corr'].values.reshape(-1, 1), axis=1)

sklearn error ValueError: Input contains NaN, infinity or a value too large for dtype('float64')

I am using sklearn and having a problem with the affinity propagation. I have built an input matrix and I keep getting the following error.
ValueError: Input contains NaN, infinity or a value too large for dtype('float64').
I have run
np.isnan(mat.any()) #and gets False
np.isfinite(mat.all()) #and gets True
I tried using
mat[np.isfinite(mat) == True] = 0
to remove the infinite values but this did not work either.
What can I do to get rid of the infinite values in my matrix, so that I can use the affinity propagation algorithm?
I am using anaconda and python 2.7.9.

This might happen inside scikit, and it depends on what you're doing. I recommend reading the documentation for the functions you're using. You might be using one which depends e.g. on your matrix being positive definite and not fulfilling that criteria.
EDIT: How could I miss that:
np.isnan(mat.any()) #and gets False
np.isfinite(mat.all()) #and gets True
is obviously wrong. Right would be:
np.any(np.isnan(mat))
and
np.all(np.isfinite(mat))
You want to check whether any of the elements are NaN, and not whether the return value of the any function is a number...

I got the same error message when using sklearn with pandas. My solution is to reset the index of my dataframe df before running any sklearn code:
df = df.reset_index()
I encountered this issue many times when I removed some entries in my df, such as
df = df[df.label=='desired_one']

This is my function (based on this) to clean the dataset of nan, Inf, and missing cells (for skewed datasets):
import pandas as pd
import numpy as np
def clean_dataset(df):
assert isinstance(df, pd.DataFrame), "df needs to be a pd.DataFrame"
df.dropna(inplace=True)
indices_to_keep = ~df.isin([np.nan, np.inf, -np.inf]).any(axis=1)
return df[indices_to_keep].astype(np.float64)

In most cases getting rid of infinite and null values solve this problem.
get rid of infinite values.
df.replace([np.inf, -np.inf], np.nan, inplace=True)
get rid of null values the way you like, specific value such as 999, mean, or create your own function to impute missing values
df.fillna(999, inplace=True)

This is the check on which it fails:
https://github.com/scikit-learn/scikit-learn/blob/0.17.X/sklearn/utils/validation.py#L51
Which says
def _assert_all_finite(X):
"""Like assert_all_finite, but only for ndarray."""
X = np.asanyarray(X)
# First try an O(n) time, O(1) space solution for the common case that
# everything is finite; fall back to O(n) space np.isfinite to prevent
# false positives from overflow in sum method.
if (X.dtype.char in np.typecodes['AllFloat'] and not np.isfinite(X.sum())
and not np.isfinite(X).all()):
raise ValueError("Input contains NaN, infinity"
" or a value too large for %r." % X.dtype)
So make sure that you have non NaN values in your input. And all those values are actually float values. None of the values should be Inf either.

The Dimensions of my input array were skewed, as my input csv had empty spaces.

With this version of python 3:
/opt/anaconda3/bin/python --version
Python 3.6.0 :: Anaconda 4.3.0 (64-bit)
Looking at the details of the error, I found the lines of codes causing the failure:
/opt/anaconda3/lib/python3.6/site-packages/sklearn/utils/validation.py in _assert_all_finite(X)
56 and not np.isfinite(X).all()):
57 raise ValueError("Input contains NaN, infinity"
---> 58 " or a value too large for %r." % X.dtype)
59
60
ValueError: Input contains NaN, infinity or a value too large for dtype('float64').
From this, I was able to extract the correct way to test what was going on with my data using the same test which fails given by the error message: np.isfinite(X)
Then with a quick and dirty loop, I was able to find that my data indeed contains nans:
print(p[:,0].shape)
index = 0
for i in p[:,0]:
if not np.isfinite(i):
print(index, i)
index +=1
(367340,)
4454 nan
6940 nan
10868 nan
12753 nan
14855 nan
15678 nan
24954 nan
30251 nan
31108 nan
51455 nan
59055 nan
...
Now all I have to do is remove the values at these indexes.

None of the answers here worked for me. This was what worked.
Test_y = np.nan_to_num(Test_y)
It replaces the infinity values with high finite values and the nan values with numbers

I had the same error, and in my case X and y were dataframes so I had to convert them to matrices first:
X = X.values.astype(np.float)
y = y.values.astype(np.float)
Edit: The originally suggested X.as_matrix() is Deprecated

Problem seems to occur in DecisionTreeClassifier input check, Try
X_train = X_train.replace((np.inf, -np.inf, np.nan), 0).reset_index(drop=True)

I had the error after trying to select a subset of rows:
df = df.reindex(index=my_index)
Turns out that my_index contained values that were not contained in df.index, so the reindex function inserted some new rows and filled them with nan.

Remove all infinite values:
(and replace with min or max for that column)
import numpy as np
# generate example matrix
matrix = np.random.rand(5,5)
matrix[0,:] = np.inf
matrix[2,:] = -np.inf
>>> matrix
array([[ inf, inf, inf, inf, inf],
[0.87362809, 0.28321499, 0.7427659 , 0.37570528, 0.35783064],
[ -inf, -inf, -inf, -inf, -inf],
[0.72877665, 0.06580068, 0.95222639, 0.00833664, 0.68779902],
[0.90272002, 0.37357483, 0.92952479, 0.072105 , 0.20837798]])
# find min and max values for each column, ignoring nan, -inf, and inf
mins = [np.nanmin(matrix[:, i][matrix[:, i] != -np.inf]) for i in range(matrix.shape[1])]
maxs = [np.nanmax(matrix[:, i][matrix[:, i] != np.inf]) for i in range(matrix.shape[1])]
# go through matrix one column at a time and replace + and -infinity
# with the max or min for that column
for i in range(matrix.shape[1]):
matrix[:, i][matrix[:, i] == -np.inf] = mins[i]
matrix[:, i][matrix[:, i] == np.inf] = maxs[i]
>>> matrix
array([[0.90272002, 0.37357483, 0.95222639, 0.37570528, 0.68779902],
[0.87362809, 0.28321499, 0.7427659 , 0.37570528, 0.35783064],
[0.72877665, 0.06580068, 0.7427659 , 0.00833664, 0.20837798],
[0.72877665, 0.06580068, 0.95222639, 0.00833664, 0.68779902],
[0.90272002, 0.37357483, 0.92952479, 0.072105 , 0.20837798]])

I found that after calling pct_change on a new column that nan existed in one of rows. I remove the nan row with the following code
df = df.replace([np.inf, -np.inf], np.nan)
df = df.dropna()
df = df.reset_index()

i got the same error. it worked with df.fillna(-99999, inplace=True) before doing any replacement, substitution etc

I would like to propose a solution for numpy that worked well for me. The line
from numpy import inf
inputArray[inputArray == inf] = np.finfo(np.float64).max
substitues all infite values of a numpy array with the maximum float64 number.

Puff !! In my case the problem was about NaN values...
You can list your columns that had NaN with this function
your_data.isnull().sum()
and then you can fill these NAN values in your dataset file.
Here is the code for how to "Replace NaN with zero and infinity with large finite numbers."
your_data[:] = np.nan_to_num(your_data)
from numpy.nan_to_num

In my case the problem was that many scikit functions return numpy arrays, which are devoid of pandas index. So there was an index mismatch when I used those numpy arrays to build new DataFrames and then I tried to mix them with the original data.

dataset = dataset.dropna(axis=0, how='any', thresh=None, subset=None, inplace=False)
This worked for me

I had the same issue, in my case the answer was simply that I had a cell in my CSV with no value ("x,y,z,,"). Putting a default value in fixed it for me.

Using isneginf may help.
http://docs.scipy.org/doc/numpy/reference/generated/numpy.isneginf.html#numpy.isneginf
x[numpy.isneginf(x)] = 0 #0 is the value you want to replace with

Note: This solution only applies if you consciously want to keep NaN entries in your dataset.
This error happened to me when I was using some of the scikit-learn functionality (in my case: GridSearchCV). Under the hood I was using an xgboost XGBClassifier which handles NaN data gracefully. However, GridSearchCV was using sklearn.utils.validation module that encforced lack of missing data in the input data by calling _assert_all_finite function. This was ultimately causing an error:
ValueError: Input contains NaN, infinity or a value too large for dtype('float64')
Sidenote: _assert_all_finite accepts an allow_nan argument, which, if set to True, would not be causing issues. However, scikit-learn API does not allow us to have control over this argument.
Solution
My solution was to use patch module to silence the _assert_all_finite function so that it does not raise ValueError. Here is a snippet
import sklearn
with mock.patch("sklearn.utils.validation._assert_all_finite"):
# your code that raises ValueError
this will replace the _assert_all_finite by a dummy mock function so it won't get executed.
Please note that patching is not a recommended practice and might result in unpredictable behaviour!
EDIT:
This Pull Request should resolve the issue (though the fix has not been released as of Jan 2022)

If you're running an estimator, it could be that your learning rate is too high. I passed in the wrong array to a grid search by accident and ended up training with a learning rate of 500, which I could see causing issues with the training process.
Basically it's not necessarily only your inputs that have to all be valid, but the intermediate data as well.

After a long time of dealing with this problem, I realized that this is because in splits of training and testing sets there are columns of data which are the same for all data rows. Then some calculations in some algorithms may lead to infinity results. If the data that you are using is in a way that close rows are more likely to be similar then shuffling the data can help. This is a bug with scikit. I'm using version 0.23.2.

If you happen to use the "kc_house_data.csv" dataset (which some commenters and many data-science newcomers seem to use, because it's presented in lots of popular course material), the data is faulty and the true source for the error.
To fix it, as of 2022:
Delete the last (empty) line in the csv file
There are two lines that contain one empty data value "x,x,,x,x" - to fix it, don't delete the comma, instead add a random integer value like 2000, so it looks like this "x,x,2000,x,x"
Don't forget to save and reload in your project.
All the other answers are helpful and correct, but not in this case:
If you use kc_house_data.csv you need to fix the data in the file, nothing else will help, the empty data field will shift the other data around randomly and generate weird bugs that are hard to track to the source!

In my case the algorithm required data to be between (0,1) noninclusive. My quite brutal solutions was to add a small random number to all desired values:
y_train = pd.DataFrame(y_train).applymap(lambda x: x + np.random.rand()/100000.0)["col_name"]
y_train[y_train >= 1] = 0.999999
while y_train is in the range of [0,1].
This is definitely not suitable for all cases, as you are messing with your input data but can be a solution if you have sparse data and only need a quick forecast

try
mat.sum()
If the sum of your data is infinity (greater that the max float value which is 3.402823e+38) you will get that error.
see the _assert_all_finite function in validation.py from the scikit source code:
if is_float and np.isfinite(X.sum()):
pass
elif is_float:
msg_err = "Input contains {} or a value too large for {!r}."
if (allow_nan and np.isinf(X).any() or
not allow_nan and not np.isfinite(X).all()):
type_err = 'infinity' if allow_nan else 'NaN, infinity'
# print(X.sum())
raise ValueError(msg_err.format(type_err, X.dtype))

Filling missing data with different methods

I have a couple of a set of data with timestamp, value and quality flag. The value and quality flag are missing for some of the timestamps, and needs to be filled with a dependence on the surrounding data. I.e.,
If the quality flags on the valid data bracketing the NaN data are different, then set the value and quality flag to the same as the bracketing row with the highest quality flag. In the example below, the first set of NaNs would be replaced with qf=3 and value=3.
If the quality flags are the same, then interpolate the value between the two valid values on either side. In the example, the second set of NaNs would be replaced by qf = 1 and v = 6 and 9.
Code:
import datetime
import pandas as pd
start = datetime.strptime("2004-01-01 00:00","%Y-%m-%d %H:%M")
end = datetime.strptime("2004-01-01 03:00","%Y-%m-%d %H:%M")
df = pd.DataFrame(\
data = {'v' : [1,2,'NaN','NaN','NaN',3,2,1,5,3,'NaN','NaN',12,43,23,12,32,12,12],\
'qf': [1,1,'NaN','NaN','NaN',3,1,5,1,1,'NaN','NaN',1,3,4,2,1,1,1]},\
index = pd.date_range(start, end,freq="10min"))
I have tried to solve this by finding the NA rows and looping through them, to fix the first criteron, then using interpolate to solve the second. However, this is really slow as I am working with a large set.

One approach would just be to do all the possible fills and then choose among them as appropriate. After doing df = df.astype(float) if necessary (your example uses the string "NaN"), something like this should work:
is_null = df.qf.isnull()
fill_down = df.ffill()
fill_up = df.bfill()
df.loc[is_null & (fill_down.qf > fill_up.qf)] = fill_down
df.loc[is_null & (fill_down.qf < fill_up.qf)] = fill_up
df = df.interpolate()
It does more work than is necessary, but it's easy to see what it's doing, and the work that it does do is vectorized and so happens pretty quickly. On a version of your dataset expanded to be ~10M rows (with the same density of nulls), it takes ~6s on my old notebook. Depending on your requirements that might suffice.