The pandas.DataFrame.interpolate() method is throwing this error when using the method='time' argument:
Error: Invalid fill method.Expecting pad(ffill) or backfill(bfill).Got timeThis only started happening after pandas anycodings_python updated to version 0.24, which seems to have anycodings_python changed some implementation. Code that used anycodings_python to work breads with version 0.24 or greater.,The pandas.DataFrame.interpolate() method is anycodings_python throwing this error when using the anycodings_python method='time' argument:,For anyone who has a similar issue, I anycodings_pandas also got this error due to the new anycodings_pandas pandas types after 1.1.x.,This is not the case anymore -- you need anycodings_pandas to explicitly cast to float and then you anycodings_pandas can interpolate. Yes, it is completely anycodings_pandas unnecessary type safety. Yes, it is a anycodings_pandas horrible error message.
The pandas.DataFrame.interpolate() method is anycodings_python throwing this error when using the anycodings_python method='time' argument:
Error: Invalid fill method.Expecting pad(ffill) or backfill(bfill).Got timeValueError: Invalid fill method. Expecting pad (ffill) or backfill (bfill). Got linear,I try: df.groupby(['lat', 'lon']).apply(lambda group: group.interpolate(method='linear')), and it throws {ValueError}Invalid fill method. Expecting pad (ffill) or backfill (bfill). Got linear I suspect the issue is with the fact that I have None values, and I do not want to interpolate those. What is the solution?, 1 week ago May 19, 2021 · ValueError: Invalid fill method. Expecting pad (ffill) or backfill (bfill). Got linear , 1 week ago Pandas Interpolation: {ValueError}Invalid fill method. Expecting pad (ffill) or backfill (bfill). Got linear ... (lambda group: group.interpolate(method='linear')), and it throws {ValueError}Invalid fill method. Expecting pad (ffill) or backfill (bfill). Got linear I suspect the issue is with the fact that I have None values, and I do not want ...
id data lat notes analysis_date 0 17358709 NaN 26.125979 None 2019 - 09 - 20 12: 00: 00 + 00: 00 1 17358709 NaN 26.125979 None 2019 - 09 - 20 12: 00: 00 + 00: 00 2 17352742 - 2.331365 26.125979 None 2019 - 09 - 20 12: 00: 00 + 00: 00 3 17358709 - 4.424366 26.125979 None 2019 - 09 - 20 12: 00: 00 + 00: 00import pandas as pd df = pd.DataFrame({
'time': pd.to_datetime(['2010', '2011', 'foo', '2012', '2013'], errors = 'coerce')
}) df['time'] = df.time.dt.tz_localize('UTC').dt.tz_convert('Asia/Kolkata') df.interpolate()Improve error reporting for DataFrame.merge() when invalid merge column definitions were given (GH16228),This is an experimental feature. Currently, many methods fail to propagate the allows_duplicate_labels value. In future versions it is expected that every method taking or returning one or more DataFrame or Series objects will propagate allows_duplicate_labels.,Improved error reporting for subsetting columns of a DataFrameGroupBy with axis=1 (GH37725),Bug in DataFrame.replace() and Series.replace() incorrectly raising an AssertionError instead of a ValueError when invalid parameter combinations are passed (GH36045)
In[1]: pd.Series([1, 2], index = ['a', 'a'])
Out[1]:
a 1
a 2
Length: 2, dtype: int64
In[2]: pd.Series([1, 2], index = ['a', 'a']).set_flags(allows_duplicate_labels = False)
...
DuplicateLabelError: Index has duplicates.
positions
label
a[0, 1]In[3]: a = (
...: pd.Series([1, 2], index = ['a', 'b'])
...: .set_flags(allows_duplicate_labels = False)
...: )
In[4]: a
Out[4]:
a 1
b 2
Length: 2, dtype: int64
# An operation introducing duplicates
In[5]: a.reindex(['a', 'b', 'a'])
...
DuplicateLabelError: Index has duplicates.
positions
label
a[0, 2]
[1 rows x 1 columns]In[1]: import io
In[2]: data = pd.DataFrame([0, 1, 2])
In[3]: buffer = io.BytesIO()
In[4]: data.to_csv(buffer, encoding = "utf-8", compression = "gzip")In[5]: data = pd.DataFrame({
'a': [1, 2],
'b': [3, 4]
})
In[6]: table = data.to_latex(position = 'ht')
In[7]: print(table)\ begin {
table
} [ht]\ centering\ begin {
tabular
} {
lrr
}\
toprule {} & a & b\\\ midrule
0 & 1 & 3\\
1 & 2 & 4\\\ bottomrule\ end {
tabular
}\
end {
table
}In[8]: data = pd.DataFrame({
'a': [1, 2],
'b': [3, 4]
})
In[9]: table = data.to_latex(caption = ('the full long caption', 'short caption'))
In[10]: print(table)\ begin {
table
}\
centering\ caption[short caption] {
the full long caption
}\
begin {
tabular
} {
lrr
}\
toprule {} & a & b\\\ midrule
0 & 1 & 3\\
1 & 2 & 4\\\ bottomrule\ end {
tabular
}\
end {
table
}# the
default NumPy float64 dtype
In[11]: s1 = pd.Series([1.5, None])
In[12]: s1
Out[12]:
0 1.5
1 NaN
dtype: float64
In[13]: s1 > 1
Out[13]:
0 True
1 False
dtype: boolHello, When I try to resample flight data using the built-in method I get a ValueError. To reproduce:,This is using the latest build of traffic (obtained with: pip install git+https://github.com/xoolive/traffic), Pandas version 0.24.1 and python version 3.6.7.,No problem. For what it's worth, I'm running in a conda environment and I know there are some problems when installing pip packages in conda - maybe that happens here.,Thank you for the feedback. I cannot reproduce the issue with my current version of pandas (0.23.4) but will do the upgrade in the coming days.
from traffic.data
import opensky
flight = opensky.history("2017-02-05", callsign = "EZY158T")
flight2 = flight.resample() File "<stdin>", line 1, in <module>
File "/network/aopp/apres/users/proud/conda/lib/python3.6/site-packages/traffic/core/flight.py", line 628, in resample
.resample(rule)
File "/network/aopp/apres/users/proud/conda/lib/python3.6/site-packages/pandas/core/generic.py", line 6835, in interpolate
**kwargs)
File "/network/aopp/apres/users/proud/conda/lib/python3.6/site-packages/pandas/core/internals/managers.py", line 519, in interpolate
return self.apply('interpolate', **kwargs)
File "/network/aopp/apres/users/proud/conda/lib/python3.6/site-packages/pandas/core/internals/managers.py", line 395, in apply
applied = getattr(b, f)(**kwargs)
File "/network/aopp/apres/users/proud/conda/lib/python3.6/site-packages/pandas/core/internals/blocks.py", line 1845, in interpolate
limit=limit),
File "/network/aopp/apres/users/proud/conda/lib/python3.6/site-packages/pandas/core/arrays/datetimelike.py", line 769, in fillna
value, method = validate_fillna_kwargs(value, method)
File "/network/aopp/apres/users/proud/conda/lib/python3.6/site-packages/pandas/util/_validators.py", line 348, in validate_fillna_kwargs
method = clean_fill_method(method)
File "/network/aopp/apres/users/proud/conda/lib/python3.6/site-packages/pandas/core/missing.py", line 91, in clean_fill_method
raise ValueError(msg)
ValueError: Invalid fill method. Expecting pad (ffill) or backfill (bfill). Got linearAligning per-level is not yet supported. Only the default, level=None, is allowed.,Per-level aggregation is not yet supported (BEAM-11777). Only the default, level=None, is allowed.,Filling NaN values via method is not supported, because it is sensitive to the order of the data (see https://s.apache.org/dataframe-order-sensitive-operations). Only the default, method=None, is allowed.,pandas.Series.values is not supported in the Beam DataFrame API because it produces an output type that is not deferred.
>>> s = pd.Series([1, 2, 3]) >>>
s
0 1
1 2
2 3
dtype: int64 >>>
s.rename("my_name") # scalar, changes Series.name
0 1
1 2
2 3
Name: my_name, dtype: int64 >>>
s.rename(lambda x: x ** 2) #
function, changes labels
0 1
1 2
4 3
dtype: int64 >>>
s.rename({
1: 3,
2: 5
}) # mapping, changes labels
0 1
3 2
5 3
dtype: int64>>> s = pd.Series([2, 0, 4, 8, np.nan])
Boundary values are included by
default:
>>>
s.between(1, 4)
0 True
1 False
2 True
3 False
4 False
dtype: bool
With `inclusive`
set to ``
False``
boundary values are excluded:
>>>
s.between(1, 4, inclusive = False)
0 True
1 False
2 False
3 False
4 False
dtype: bool
`left`
and `right`
can be any scalar value:
>>>
s = pd.Series(['Alice', 'Bob', 'Carol', 'Eve']) >>>
s.between('Anna', 'Daniel')
0 False
1 True
2 True
3 False
dtype: bool>>> s = pd.Series([1, 2, 3, 4]) >>>
s
0 1
1 2
2 3
3 4
dtype: int64
>>>
s.add_suffix('_item')
0_ item 1
1_ item 2
2_ item 3
3_ item 4
dtype: int64
>>>
df = pd.DataFrame({
'A': [1, 2, 3, 4],
'B': [3, 4, 5, 6]
}) >>>
df
A B
0 1 3
1 2 4
2 3 5
3 4 6
>>>
df.add_suffix('_col')
A_col B_col
0 1 3
1 2 4
2 3 5
3 4 6>>> s = pd.Series([1, 2, 3, 4]) >>>
s
0 1
1 2
2 3
3 4
dtype: int64
>>>
s.add_prefix('item_')
item_0 1
item_1 2
item_2 3
item_3 4
dtype: int64
>>>
df = pd.DataFrame({
'A': [1, 2, 3, 4],
'B': [3, 4, 5, 6]
}) >>>
df
A B
0 1 3
1 2 4
2 3 5
3 4 6
>>>
df.add_prefix('col_')
col_A col_B
0 1 3
1 2 4
2 3 5
3 4 6Show which entries in a DataFrame are NA.
>>>
df = pd.DataFrame(dict(age = [5, 6, np.NaN],
...born = [pd.NaT, pd.Timestamp('1939-05-27'),
...pd.Timestamp('1940-04-25')
],
...name = ['Alfred', 'Batman', ''],
...toy = [None, 'Batmobile', 'Joker'])) >>>
df
age born name toy
0 5.0 NaT Alfred None
1 6.0 1939 - 05 - 27 Batman Batmobile
2 NaN 1940 - 04 - 25 Joker
>>>
df.isna()
age born name toy
0 False True False True
1 False False False False
2 True False False False
Show which entries in a Series are NA.
>>>
ser = pd.Series([5, 6, np.NaN]) >>>
ser
0 5.0
1 6.0
2 NaN
dtype: float64
>>>
ser.isna()
0 False
1 False
2 True
dtype: boolShow which entries in a DataFrame are not NA.
>>>
df = pd.DataFrame(dict(age = [5, 6, np.NaN],
...born = [pd.NaT, pd.Timestamp('1939-05-27'),
...pd.Timestamp('1940-04-25')
],
...name = ['Alfred', 'Batman', ''],
...toy = [None, 'Batmobile', 'Joker'])) >>>
df
age born name toy
0 5.0 NaT Alfred None
1 6.0 1939 - 05 - 27 Batman Batmobile
2 NaN 1940 - 04 - 25 Joker
>>>
df.notna()
age born name toy
0 True False True False
1 True True True True
2 False True True True
Show which entries in a Series are not NA.
>>>
ser = pd.Series([5, 6, np.NaN]) >>>
ser
0 5.0
1 6.0
2 NaN
dtype: float64
>>>
ser.notna()
0 True
1 True
2 False
dtype: bool