7 examples of 'pandas replace nan with none' in Python

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135def replace_nan(value, default=0):
136 if math.isnan(value):
137 return default
138 return value
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36def _replace_nan(a, val):
37 """
38 If `a` is of inexact type, make a copy of `a`, replace NaNs with
39 the `val` value, and return the copy together with a boolean mask
40 marking the locations where NaNs were present. If `a` is not of
41 inexact type, do nothing and return `a` together with a mask of None.
42
43 Note that scalars will end up as array scalars, which is important
44 for using the result as the value of the out argument in some
45 operations.
46
47 Parameters
48 ----------
49 a : array-like
50 Input array.
51 val : float
52 NaN values are set to val before doing the operation.
53
54 Returns
55 -------
56 y : ndarray
57 If `a` is of inexact type, return a copy of `a` with the NaNs
58 replaced by the fill value, otherwise return `a`.
59 mask: {bool, None}
60 If `a` is of inexact type, return a boolean mask marking locations of
61 NaNs, otherwise return None.
62
63 """
64 a = np.array(a, subok=True, copy=True)
65
66 if a.dtype == np.object_:
67 # object arrays do not support `isnan` (gh-9009), so make a guess
68 mask = a != a
69 elif issubclass(a.dtype.type, np.inexact):
70 mask = np.isnan(a)
71 else:
72 mask = None
73
74 if mask is not None:
75 np.copyto(a, val, where=mask)
76
77 return a, mask
74def filter_nan2none(value):
75 """Convert the NaN value to None, leaving everything else unchanged.
76
77 This function is meant to be used as a Django template filter. It
78 is useful in combination with filters that handle None (or any
79 false value) specially, such as the 'default' filter, when one
80 wants special treatment for the NaN value. It is also useful
81 before the 'format' filter to avoid the NaN value being formatted.
82
83 """
84 if is_nan(value):
85 return None
86 return value
24def fix_nans(mat):
25 """
26 returns the matrix with average over models if a model, sample, chromosome had nan in it.
27 :param mat: ndarray (model, sample, chromosome)
28 :return: mat ndarray (model, sample, chromosome)
29 """
30 mat = np.nan_to_num(mat)
31 idx, idy, idz = np.where(mat == 0)
32 for x, y, z in zip(idx, idy, idz):
33 mat[x, y, z] = mat[:, y, z].mean()
34 return mat
19def fill_missing_values(df_data):
20 """Fill missing values in data frame, in place."""
21 df_data.fillna(method="ffill", inplace="True")
22 df_data.fillna(method='bfill', inplace="True")
203def nan_to_zero(segment: Union[pd.Series, list], nan_list: list) -> Union[pd.Series, list]:
204 if type(segment) == pd.Series:
205 for val in nan_list:
206 segment.values[val] = 0
207 else:
208 for val in nan_list:
209 segment[val] = 0
210 return segment
20def none_missing(df, columns=None):
21 """
22 Asserts that there are no missing values (NaNs) in the DataFrame.
23
24 Parameters
25 ----------
26 df : DataFrame
27 columns : list
28 list of columns to restrict the check to
29
30 Returns
31 -------
32 df : DataFrame
33 same as the original
34 """
35 if columns is None:
36 columns = df.columns
37 try:
38 assert not df[columns].isnull().any().any()
39 except AssertionError as e:
40 missing = df[columns].isnull()
41 msg = generic.bad_locations(missing)
42 e.args = msg
43 raise
44 return df

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