## Python get coordinate with pair of nans

split latitude and longitude coordinates python
split coordinates python pandas
sklearn euclidean distance
python euclidean distance matrix
name euclidean_distances is not defined
pairwise euclidean distance
sklearn paired distances
sklearn pairwise distance

I have a dataframe like this

```>>df1 = pd.DataFrame({
'A': ['1', '2', '3', '4', '5'],
'B': ['1', '1', '1', '1', '1'],
'C': ['c', 'A1', NaN, 'c3', Nan],
'D': ['d0', 'B1', 'B2', Nan, 'B4'],
'E': ['A', Nan, 'S', Nan, 'S'],
'F': ['3', '4', '5', '6', '7'],
'G': ['2', '2', NaN, '2', '2']
})
>>df1

A   B     C     D     E   F     G
0   1   1     c    d0     A   3     2
1   2   1    A1    B1   NaN   4     2
2   3   1   NaN    B2     S   5   NaN
3   4   1    c3   NaN   NaN   6     2
4   5   1   NaN    B4     S   7     2
```

and I would like to get the coordinates of all nans. that is the output should be:

```[[1,"E"], [2,"C"] , [2,"G"] , [3,"D"] ,[3,"E"] , [4,"C"] ]
```

All other questions i looked at just want the column name and not the pairs.

Is there any efficient way to solve this problem? Thank you

Use `stack` with filter index values by missing values:

```s = df1.stack(dropna=False)
L = [list(x) for x in s.index[s.isna()]]
print (L)
[[1, 'E'], [2, 'C'], [2, 'G'], [3, 'D'], [3, 'E'], [4, 'C']]
```

sklearn.metrics.pairwise.nan_euclidean_distances — scikit-learn , scikit-learn: machine learning in Python. Compute the euclidean distance between each pair of samples in X and Y, where Y=X is If all the coordinates are missing or if there are no common present coordinates then NaN is returned for that pair. get distance to origin >>> nan_euclidean_distances(X, [[0, 0]]) array([[1. ]� Python assigns an id to each variable that is created, and ids are compared when Python looks at the identity of a variable in an operation. However, np.nan is a single object that always has the same id, no matter which variable you assign it to. import numpy as np one = np.nan two = np.nan one is two. np.nan is np.nan is True and one is two is also True.

Try using np.where:

```df = pd.DataFrame({'A': ['1', '2', '3', '4','5'],
'B': ['1', '1', '1', '1','1'],
'C': ['c', 'A1', np.nan, 'c3',np.nan],
'D': ['d0', 'B1', 'B2', np.nan,'B4'],
'E': ['A', np.nan, 'S', np.nan,'S'],
'F': ['3', '4', '5', '6','7'],
'G': ['2', '2', np.nan, '2','2']})

arr = np.where(df.isna())
arr
(array([1, 2, 2, 3, 3, 4], dtype=int64),
array([4, 2, 6, 3, 4, 2], dtype=int64))
```

np.where returns the indices where the given condition is True, here where df is null.

```[(x, df.columns[y]) for x, y in zip(arr[0], arr[1])]

[(1, 'E'), (2, 'C'), (2, 'G'), (3, 'D'), (3, 'E'), (4, 'C')]
```

Split Lat/Long Coordinate Variables Into Separate Variables, Try my machine learning flashcards or Machine Learning with Python Cookbook. Split Lat/Long Coordinate Variables Into Separate Variables if you get an error except: # append a missing value to lat lat.append(np. NaN) # Create two new columns from lat and lon df['latitude'] = lat df['longitude'] = lon� You obtained the same result as with the pure Python implementation. If you have nan values in a dataset, then gmean() will return nan. If there’s at least one 0, then it’ll return 0.0 and give a warning. If you provide at least one negative number, then you’ll get nan and the warning. Median. The sample median is the middle element of a

You could use np.argwhere with pd.isna, like this:

```result = [[r, df1.columns[c]] for r, c in np.argwhere(pd.isna(df1).values)]
print(result)
```

Output

```[[1, 'E'], [2, 'C'], [2, 'G'], [3, 'D'], [3, 'E'], [4, 'C']]
```

Geocoding And Reverse Geocoding, Python offers a number of packages to make the task incredibly easy. dataframe structures, and numpy for its missing value (np.nan) functionality. strings, with each coordinate in a coordinate pair separated by a comma. comma to lon lon.append(float(row.split(',')[1])) # But if you get an error except:� Python Tuples. Python provides another type that is an ordered collection of objects, called a tuple. Pronunciation varies depending on whom you ask. Some pronounce it as though it were spelled “too-ple” (rhyming with “Mott the Hoople”), and others as though it were spelled “tup-ple” (rhyming with “supple”).

How to find the indices of rows in a pandas DataFrame containing , Kite is a free autocomplete for Python developers. DataFrame containing NaN values results in a list of the indices of the rows in the DataFrame.iterrows() to iterate over each index, row pair in pandas. Get Kite updates & coding tips. import arcpy # Set the workspace environment arcpy.env.workspace = "c:/base/base.gdb" # Get a list of the feature classes in the input folder feature_classes = arcpy.ListFeatureClasses() # Loop through the list for fc in feature_classes: # Create the spatial reference object spatial_ref = arcpy.Describe(fc).spatialReference # If the spatial reference is unknown if spatial_ref.name == "Unknown

NumPy, SciPy, and Pandas: Correlation With Python – Real Python, Each of these x-y pairs represents a single observation. In Python, nan is a special floating-point value that you can get by using any of the following: can access them with either their labels or their integer position indices:. scipy.ndimage.map_coordinates¶ scipy.ndimage.map_coordinates (input, coordinates, output = None, order = 3, mode = 'constant', cval = 0.0, prefilter = True) [source] ¶ Map the input array to new coordinates by interpolation. The array of coordinates is used to find, for each point in the output, the corresponding coordinates in the input.

Outputs `NaN,NaN` for invalid geometry centroids � Issue #31, Do we ignore the NaN coordinates and keep the rest of the geometry I tried to load the file as dumped by pyesridump (including the NaN 's) in the source but got: File "/usr/local/lib/python3.4/dist-packages/ijson/backends/python.py", Do we skip the coordinate pair or do we ditch the whole geometry? Python | Pandas dataframe.get_value() 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.