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Pandas fillna values with numpy array


Pandas fillna values with numpy array

By : vijaykanth
Date : November 22 2020, 02:42 PM
may help you . If you're sure that arr is the same length as the number of times 'foo' appears, you can use the following to set the values:
code :
df.loc[df['A'] == 'foo', 'B'] = arr


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Pandas fillna: Output still has NaN values

Pandas fillna: Output still has NaN values


By : Ethan Zaritskyi
Date : March 29 2020, 07:55 AM
Hope that helps You can just fillna with the df.mean() Series (which is dict-like):
code :
In [11]: df = pd.DataFrame([[1, np.nan], [np.nan, 4], [5, 6]])

In [12]: df
Out[12]:
    0   1
0   1 NaN
1 NaN   4
2   5   6

In [13]: df.fillna(df.mean())
Out[13]:
   0  1
0  1  5
1  3  4
2  5  6
In [14]: df.mean()
Out[14]:
0    3
1    5
dtype: float64
df.mean().fillna(0)
df.fillna(df.mean().fillna(0))
Python: Pandas Dataframe AttributeError: 'numpy.ndarray' object has no attribute 'fillna'

Python: Pandas Dataframe AttributeError: 'numpy.ndarray' object has no attribute 'fillna'


By : joan didi
Date : March 29 2020, 07:55 AM
it helps some times (M - 3) is getting interpreted as a numpy.ndarray. This implies that M is defined somewhere as a numpy.ndarray. Test it out by running:
code :
print type(M)
Replace 0 by the last value seen in the array (similar to the fillna method with NaN values) in pandas dataframes

Replace 0 by the last value seen in the array (similar to the fillna method with NaN values) in pandas dataframes


By : Mr. Nb
Date : March 29 2020, 07:55 AM
I wish did fix the issue. This should be a bit faster (about 6x for 200,000 rows but check for yourself, of course). After import numpy as np:
code :
arr = np.select( [df1==1,df2==1], [1,0], default=np.nan )
ser = pd.Series( arr ).ffill()
array([  1.,   1.,   1.,  nan,  nan,  nan,   0.,   0.,  nan,  nan,  nan,
        nan,  nan,  nan,   1.,  nan,  nan,   1.,  nan,  nan,   0.,  nan,
        nan,  nan,  nan,   1.,   1.,   1.,  nan,  nan,  nan,  nan,  nan,
         0.,  nan,  nan,   0.,  nan,  nan,  nan,  nan,  nan,  nan,   1.,
         1.,   1.,  nan,  nan,  nan,  nan])
How to use numpy fillna() with numpy.where() for a column in a pandas DataFrame?

How to use numpy fillna() with numpy.where() for a column in a pandas DataFrame?


By : Sameion A
Date : March 29 2020, 07:55 AM
Hope that helps Here is an example pandas DataFrame: , fillna is base on index
code :
df['New']=np.where(df1['type']=='B', df1['front'], df1['front'] + df1['back'])
df
Out[125]: 
   amount      back       file     front type       end       New
0       3  21973805  filename2  21889611    A       NaN  43863416
1       4  36403870  filename2  36357723    A       NaN  72761593
2       5    277500  filename3    196312    A  473812.0    473812
3       1        19  filename4        11    B       NaN        11
4       2       120  filename4        42    B      42.0        42
5       1      3210  filename3      1992    C       NaN      5202
df.end.fillna(df.New)
Out[126]: 
0    43863416.0
1    72761593.0
2      473812.0
3          11.0
4          42.0
5        5202.0
Name: end, dtype: float64
df.end=df.end.fillna(df.New)
df
Out[128]: 
   amount      back       file     front type         end       New
0       3  21973805  filename2  21889611    A  43863416.0  43863416
1       4  36403870  filename2  36357723    A  72761593.0  72761593
2       5    277500  filename3    196312    A    473812.0    473812
3       1        19  filename4        11    B        11.0        11
4       2       120  filename4        42    B        42.0        42
5       1      3210  filename3      1992    C      5202.0      5202
df['New']=np.where(df1['type']=='B', df1['front'], df1['front'] + df1['back'])
df.end=df.end.fillna(df.New)
df
Out[133]: 
   amount      back       file     front type         end       New
0       3  21973805  filename2  21889611    A  43863416.0  43863416
1       4  36403870  filename2  36357723    A  72761593.0  72761593
2       5    277500  filename3    196312    A        12.0    473812
3       1        19  filename4        11    B        11.0        11
4       2       120  filename4        42    B        49.0        42
5       1      3210  filename3      1992    C      5202.0      5202
Fillna PySpark Dataframe with numpy array Error

Fillna PySpark Dataframe with numpy array Error


By : GuitarMyke
Date : March 29 2020, 07:55 AM
around this issue DataFrameNaFunctions support only a subset of native (no UDTs) types, so you'll need an UDF here.
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