I have a dataframe as shown below
Date t_factor t1 t2 t3 t_function
2020-02-01 5 4 NaN NaN 4
2020-02-03 23 6 NaN NaN 6
2020-02-06 14 9 NaN NaN 9
2020-02-09 23 NaN NaN NaN 0
2020-02-10 23 NaN NaN NaN 0
2020-02-11 23 NaN NaN NaN 0
2020-02-13 30 NaN 3 NaN 3
2020-02-20 29 NaN 66 NaN 66
2020-02-29 100 NaN 291 NaN 291
2020-03-01 38 NaN NaN NaN 0
2020-03-10 38 NaN NaN NaN 0
2020-03-11 38 NaN NaN 4 4
2020-03-26 70 NaN NaN 4 4
2020-03-29 70 NaN NaN 4 4
In which I would like to fill NaN values after non NaN value as last NaN value of that column
Here the columns I wanted to impute are t1, t2 and t3.
Expected Output
Date t_factor t1 t2 t3 t_function
2020-02-01 5 4 NaN NaN 4
2020-02-03 23 6 NaN NaN 6
2020-02-06 14 9 NaN NaN 9
2020-02-09 23 9 NaN NaN 0
2020-02-10 23 9 NaN NaN 0
2020-02-11 23 9 NaN NaN 0
2020-02-13 30 9 3 NaN 3
2020-02-20 29 9 66 NaN 66
2020-02-29 100 9 291 NaN 291
2020-03-01 38 9 291 NaN 0
2020-03-10 38 9 291 NaN 0
2020-03-11 38 9 291 4 4
2020-03-26 70 9 291 4 4
2020-03-29 70 9 291 4 4
Use ffill:
df[['t1', 't2', 't3']] = df[['t1', 't2', 't3']].ffill()
Result:
Date t_factor t1 t2 t3 t_function
0 2020-02-01 5 4.0 NaN NaN 4
1 2020-02-03 23 6.0 NaN NaN 6
2 2020-02-06 14 9.0 NaN NaN 9
3 2020-02-09 23 9.0 NaN NaN 0
4 2020-02-10 23 9.0 NaN NaN 0
5 2020-02-11 23 9.0 NaN NaN 0
6 2020-02-13 30 9.0 3.0 NaN 3
7 2020-02-20 29 9.0 66.0 NaN 66
8 2020-02-29 100 9.0 291.0 NaN 291
9 2020-03-01 38 9.0 291.0 NaN 0
10 2020-03-10 38 9.0 291.0 NaN 0
11 2020-03-11 38 9.0 291.0 4.0 4
12 2020-03-26 70 9.0 291.0 4.0 4
13 2020-03-29 70 9.0 291.0 4.0 4
We can define a function for that
def imporove(iterable):
for i in range(len(iterable)):
if iterable[i].isnull() == True:
iterable[i] = iterable[i-1]
I hope you got a basic idea.
now you can pass
df['t1'].apply(improve)
Here is how I will go:
def fill_na(col):
ind = df[col].last_valid_index()
df[col][ind+1:].fillna(df[col][ind], inplace=True)
fill_na('t1')
fill_na('t2')
fill_na('t3')
Related
I have a data frame looks like this:
Date Demand Forecast Error [Error] Error% Error^2
1 47
2 70
3 95
4 122 61 51 51 54 23
5 142 86 34 51 54 23
6 155 110 34 51 45 36
7 189 130 54 51 45 86
8 208 152
9 160 174
10 142 176
11 160
12 160
13 160
and the df2 that looks like:
Bias MAPE MAE RMSE_rel
0 0.143709 0.273529 42.285714 43.198692
1 22.952381 0.273529 0.264758 0.270475
the df2 would be transposed with new columns absulote,scaled to look like this:
df2.set_index('Bias').T
df2.columns= ["Absulote", "Scaled"]
Absulote Scaled
MAPE 0.273529 0.273529
MAE 42.285714 0.264758
RMSE_rel 43.198692 0.270475
and there is no Bias
to Concatenate both I do this :
complete_df = pd.concat([df1, df2],axis=0, ignore_index=True)
I get this result:
Demand Forecast Error Absulote Scaled
0 47.0 NaN NaN NaN NaN
1 70.0 NaN NaN NaN NaN
2 95.0 NaN NaN NaN NaN
3 122.0 70.666667 51.333333 NaN NaN
4 142.0 95.666667 46.333333 NaN NaN
5 155.0 119.666667 35.333333 NaN NaN
6 189.0 139.666667 49.333333 NaN NaN
7 208.0 162.000000 46.000000 NaN NaN
8 160.0 184.000000 -24.000000 NaN NaN
9 142.0 185.666667 -43.666667 NaN NaN
10 NaN 170.000000 NaN NaN NaN
11 NaN NaN NaN NaN NaN
12 NaN NaN NaN NaN NaN
13 NaN NaN NaN NaN NaN
14 NaN NaN NaN 0.273529 0.273529
15 NaN NaN NaN 42.285714 0.264758
16 NaN NaN NaN 43.198692 0.270475
there is no Bias MAPE MAE RMSE_rel
I want the result to imitate the following :
is there anyway to achieve that?
I have a df as shown below.
Date t_factor
2020-02-01 5
2020-02-03 23
2020-02-06 14
2020-02-09 23
2020-02-10 23
2020-02-11 23
2020-02-13 30
2020-02-20 29
2020-02-29 100
2020-03-01 38
2020-03-10 38
2020-03-11 38
2020-03-26 70
2020-03-29 70
From that I would like to create a function that will calculate the column called t_function based on the calculated values t1, t2 and t3.
where input parameters are stored in a dictionary as shown below.
d1 = {'b1': {'s': '2020-02-01', 'e':'2020-02-06', 'coef':[3, 1, 0]},
'b2': {'s': '2020-02-13', 'e':'2020-02-29', 'coef':[2, 0, 1]},
'b3': {'s': '2020-03-11', 'e':'2020-03-29', 'coef':[4, 0, 0]}}
Expected output:
Date t_factor t1 t2 t3 t_function
2020-02-01 5 4 NaN NaN 4
2020-02-03 23 6 NaN NaN 6
2020-02-06 14 9 NaN NaN 9
2020-02-09 23 NaN NaN NaN 0
2020-02-10 23 NaN NaN NaN 0
2020-02-11 23 NaN NaN NaN 0
2020-02-13 30 NaN 3 NaN 3
2020-02-20 29 NaN 66 NaN 66
2020-02-29 100 NaN 291 NaN 291
2020-03-01 38 NaN NaN NaN 0
2020-03-10 38 NaN NaN NaN 0
2020-03-11 38 NaN NaN 4 4
2020-03-26 70 NaN NaN 4 4
2020-03-29 70 NaN NaN 4 4
I tried below code
def fun(x, start="2020-02-01", end="2020-02-06", a0=3, a1=1, a2=0):
start = datetime.strptime(start, "%Y-%m-%d")
end = datetime.strptime(end, "%Y-%m-%d")
if start <= x.Date <= end:
t2 = (x.Date - start)/np.timedelta64(1, 'D') + 1
diff = a0 + a1*t2 + a2*(t2)**2
else:
diff = np.NaN
return diff
df["t1"] = df.apply(lambda x: fun(x), axis=1)
df["t2"] = df.apply(lambda x: fun(x, "2020-02-13", "2020-02-29", 2, 0, 1), axis=1)
df["t3"] = df.apply(lambda x: fun(x, "2020-03-11", "2020-03-29", 4, 0, 0), axis=1)
df["t_function"] = df['t1'].fillna(0) + df['t2'].fillna(0) + df['t3'].fillna(0)
Above code I would like change by looping over the dictionary d1.
Note:
The dictionary d1 may have more than three keys such as 'b1', 'b2', 'b3', 'b4' then we have to create t1, t2, t3 and t4 columns. I would like to automate this with looping over the dictionary d1:
I would propose that you store the data as a list of tuples. Like so,
params = [('2020-02-01', '2020-02-06', 3, 1, 0),
('2020-02-13', '2020-02-29', 2, 0, 1),
('2020-03-11', '2020-03-29', 4, 0, 0)]
Now all you need is to loop over params and add the columns to your dataframe df.
total = None
for i, param in enumerate(params):
s, e, a0, a1, a2 = param
df[f"t{i+1}"] = df.apply(lambda x: fun(x, s, e, a0, a1, a2), axis=1)
if i==0:
total = df[f"t{i+1}"].fillna(0)
else:
total += df[f"t{i+1}"].fillna(0)
df["t_function"] = total
This gives the desired output:
Date t_factor t1 t2 t3 t_function
0 2020-02-01 5 4.0 NaN NaN 4.0
1 2020-02-03 23 6.0 NaN NaN 6.0
2 2020-02-06 14 9.0 NaN NaN 9.0
3 2020-02-09 23 NaN NaN NaN 0.0
4 2020-02-10 23 NaN NaN NaN 0.0
5 2020-02-11 23 NaN NaN NaN 0.0
6 2020-02-13 30 NaN 3.0 NaN 3.0
7 2020-02-20 29 NaN 66.0 NaN 66.0
8 2020-02-29 100 NaN 291.0 NaN 291.0
9 2020-03-01 38 NaN NaN NaN 0.0
10 2020-03-10 38 NaN NaN NaN 0.0
11 2020-03-11 38 NaN NaN 4.0 4.0
12 2020-03-26 70 NaN NaN 4.0 4.0
13 2020-03-29 70 NaN NaN 4.0 4.0
I have df as shown below.
Date t_factor
2020-02-01 5
2020-02-03 -23
2020-02-06 14
2020-02-09 23
2020-02-10 -2
2020-02-11 23
2020-02-13 NaN
2020-02-20 29
From the above I would like to replace -ve values in a column t_factor as NaN
Expected output:
Date t_factor
2020-02-01 5
2020-02-03 NaN
2020-02-06 14
2020-02-09 23
2020-02-10 NaN
2020-02-11 23
2020-02-13 NaN
2020-02-20 29
You can use pandas clip implementation as well. This assigns values outside boundary to boundary values. And then chain this with a replace function as below:
df['t_factor'] = df['t_factor'].clip(-1).replace(-1, np.nan)
df
Output:
Date t_factor
0 2020-02-01 5.0
1 2020-02-03 NaN
2 2020-02-06 14.0
3 2020-02-09 23.0
4 2020-02-10 NaN
5 2020-02-11 23.0
6 2020-02-13 NaN
7 2020-02-20 29.0
Use Series.mask:
df['t_factor'] = df['t_factor'].mask(df['t_factor'].lt(0))
OR use boolean indexing and assign np.nan,
df.loc[df['t_factor'].lt(0), 't_factor'] = np.nan
Result:
Date t_factor
0 2020-02-01 5.0
1 2020-02-03 NaN
2 2020-02-06 14.0
3 2020-02-09 23.0
4 2020-02-10 NaN
5 2020-02-11 23.0
6 2020-02-13 NaN
7 2020-02-20 29.0
Use pd.Series.where - by default it will replace values where the condition is False with NaN.
df["t_factor"] = df.t_factor.where(df.t_factor > 0)
This may be a different in requirements
i have data frame
A B C
1 2 4
2 4 6
8 10 12
1 3 5
and a dynamic list (the length may vary
list[1,2,3,4,5,6,7,8,9,10,11]
I wish to add C colum value with list value with each of this list and generate new dataframe column with added value how to do this?
A B C C_1 C_2 .......................... C_11
1 2 4 5 6 15
2 4 6 7 8 17
8 10 12 11 14
1 3 5 6 7 16
Thank you for your support
you can use a dict comprehension to create a simple dataframe.
dynamic_vals = [1,2,3,4,5,6,7,8,9,10,11]
df2 = pd.concat(
[df,pd.DataFrame({f'C_{val}' : [0] for val in dynamic_vals })]
,axis=1).fillna(0)
print(df2)
A B C C_1 C_2 C_3 C_4 C_5 C_6 C_7 C_8 C_9 C_10 C_11
0 1 2 4 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
1 2 4 6 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
2 8 10 12 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
3 1 3 5 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
or you could use assign again with the suggestion of #piRSqaured
df2 = df.assign(**dict((f'C_{i}', np.nan) for i in dynamic_vals))
A B C C_1 C_2 C_3 C_4 C_5 C_6 C_7 C_8 C_9 C_10 C_11
0 1 2 4 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN
1 2 4 6 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN
2 8 10 12 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN
3 1 3 5 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN
or better and more simple solution suggested by #piRSquare
df.join(pd.DataFrame(np.nan, df.index, dynamic_vals).add_prefix('C_')
A B C C_1 C_2 C_3 C_4 C_5 C_6 C_7 C_8 C_9 C_10 C_11
0 1 2 4 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN
1 2 4 6 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN
2 8 10 12 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN
3 1 3 5 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN
Edit :
Using df.join with a dictionary comprehension.
df.join(pd.DataFrame({f'C_{val}' : df['C'].values + val for val in dynamic_vals }))
A B C C_1 C_2 C_3 C_4 C_5 C_6 C_7 C_8 C_9 C_10 C_11
0 1 2 4 5 6 7 8 9 10 11 12 13 14 15
1 2 4 6 7 8 9 10 11 12 13 14 15 16 17
2 8 10 12 13 14 15 16 17 18 19 20 21 22 23
3 1 3 5 6 7 8 9 10 11 12 13 14 15 16
I've just started with pandas some weeks ago and now I am trying to perform an element-wise division on rows, but couldn't figure out the proper way to achieve it. Here is my case and data
date type id ... 1096 1097 1098
0 2014-06-13 cal 1 ... 17.949524 16.247619 15.465079
1 2014-06-13 cow 32 ... 0.523429 -0.854286 -1.520952
2 2014-06-13 cow 47 ... 7.676000 6.521714 5.892381
3 2014-06-13 cow 107 ... 4.161714 3.048571 2.419048
4 2014-06-13 cow 137 ... 3.781143 2.557143 1.931429
5 2014-06-13 cow 255 ... 3.847273 2.509091 1.804329
6 2014-06-13 cow 609 ... 6.097714 4.837714 4.249524
7 2014-06-13 cow 721 ... 3.653143 2.358286 1.633333
8 2014-06-13 cow 817 ... 6.044571 4.934286 4.373333
9 2014-06-13 cow 837 ... 9.649714 8.511429 7.884762
10 2014-06-13 cow 980 ... 1.817143 0.536571 -0.102857
11 2014-06-13 cow 1730 ... 8.512571 7.114286 6.319048
12 2014-06-13 dark 1 ... 168.725714 167.885715 167.600001
my_data.columns
Index(['date', 'type', 'id', '188', '189', '190', '191', '192', '193', '194',
...
'1089', '1090', '1091', '1092', '1093', '1094', '1095', '1096', '1097',
'1098'],
dtype='object', length=914)
My goal is to divide all the rows by the row with "type" == "cal", but from the column '188' to the column '1098' (911 columns)
These are the approaches I have tried:
Extracting the row of interest and using it with apply(), divide() and
operator '/':
>>> cal_r = my_data[my_data["type"]=="cal"].iloc[:,3:]
my_data.apply(lambda x: x.iloc[3:]/cal_r, axis=1)
0 188 189 190 191 192 193 194 195 ... 1091 10...
1 188 189 190 ... 10...
2 188 189 190 ... 109...
3 188 189 190 ... 1096...
4 188 189 190 191 ... ...
5 188 189 190 ... 10...
6 188 189 190 ... 109...
7 188 189 190 ... 1096...
8 188 189 190 ... 1096...
9 188 189 190 ... 1096 ...
10 188 189 190 ... 1...
11 188 189 190 ... 109...
12 188 189 190 191 ... ...
dtype: object
>>> mydata.apply(lambda x: x.iloc[3:].divide(cal_r,axis=1), axis=1)
Traceback (most recent call last):
File "<input>", line 1, in <module>
File "/usr/local/lib/python3.5/dist-packages/pandas/core/frame.py", line 6014, in apply
return op.get_result()
File "/usr/local/lib/python3.5/dist-packages/pandas/core/apply.py", line 142, in get_result
return self.apply_standard()
File "/usr/local/lib/python3.5/dist-packages/pandas/core/apply.py", line 248, in apply_standard
self.apply_series_generator()
File "/usr/local/lib/python3.5/dist-packages/pandas/core/apply.py", line 277, in apply_series_generator
results[i] = self.f(v)
File "<input>", line 1, in <lambda>
File "/usr/local/lib/python3.5/dist-packages/pandas/core/ops.py", line 1375, in flex_wrapper
self._get_axis_number(axis)
File "/usr/local/lib/python3.5/dist-packages/pandas/core/generic.py", line 375, in _get_axis_number
.format(axis, type(self)))
ValueError: ("No axis named 1 for object type <class 'pandas.core.series.Series'>", 'occurred at index 0')
Without using apply:
>>> my_data.iloc[:,3:].divide(cal_r)
188 189 190 191 192 193 ... 1093 1094 1095 1096 1097 1098
0 1.0 1.0 1.0 1.0 1.0 1.0 ... 1.0 1.0 1.0 1.0 1.0 1.0
1 NaN NaN NaN NaN NaN NaN ... NaN NaN NaN NaN NaN NaN
2 NaN NaN NaN NaN NaN NaN ... NaN NaN NaN NaN NaN NaN
3 NaN NaN NaN NaN NaN NaN ... NaN NaN NaN NaN NaN NaN
4 NaN NaN NaN NaN NaN NaN ... NaN NaN NaN NaN NaN NaN
5 NaN NaN NaN NaN NaN NaN ... NaN NaN NaN NaN NaN NaN
6 NaN NaN NaN NaN NaN NaN ... NaN NaN NaN NaN NaN NaN
7 NaN NaN NaN NaN NaN NaN ... NaN NaN NaN NaN NaN NaN
8 NaN NaN NaN NaN NaN NaN ... NaN NaN NaN NaN NaN NaN
9 NaN NaN NaN NaN NaN NaN ... NaN NaN NaN NaN NaN NaN
10 NaN NaN NaN NaN NaN NaN ... NaN NaN NaN NaN NaN NaN
11 NaN NaN NaN NaN NaN NaN ... NaN NaN NaN NaN NaN NaN
12 NaN NaN NaN NaN NaN NaN ... NaN NaN NaN NaN NaN NaN
The commands my_data.iloc[:,3:].divide(cal_r, axis=1) and my_data.iloc[:,3:]/cal_r give the same result, divides just the first row.
If I select just one row, it is done well:
my_data.iloc[5,3:]/cal_r
188 189 190 ... 1096 1097 1098
0 48.8182 48.8274 22.4476 ... 0.214338 0.154428 0.116671
[1 rows x 911 columns]
Is there something basic I am missing? I suspect that I will need to replicate the cal_r row the same number of rows of the whole data.
Any hint or guidance is really appreciated.
Related: divide pandas dataframe elements by its line max
I believe you need convert Series to numpy array for divide by 1d array:
cal_r = my_data.iloc[(my_data["type"]=="cal").values, 3:]
print (cal_r)
1096 1097 1098
0 17.949524 16.247619 15.465079
my_data.iloc[:, 3:] /= cal_r.values
print (my_data)
date type id 1096 1097 1098
0 2014-06-13 cal 1 1.000000 1.000000 1.000000
1 2014-06-13 cow 32 0.029161 -0.052579 -0.098348
2 2014-06-13 cow 47 0.427644 0.401395 0.381012
3 2014-06-13 cow 107 0.231857 0.187632 0.156420
4 2014-06-13 cow 137 0.210654 0.157386 0.124890
5 2014-06-13 cow 255 0.214338 0.154428 0.116671
6 2014-06-13 cow 609 0.339715 0.297749 0.274782
7 2014-06-13 cow 721 0.203523 0.145147 0.105614
8 2014-06-14 cow 817 0.336754 0.303693 0.282788
9 2014-06-14 cow 837 0.537603 0.523857 0.509843
10 2014-06-14 cow 980 0.101236 0.033025 -0.006651
11 2014-06-14 cow 1730 0.474251 0.437866 0.408601
12 2014-06-14 dark 1 9.400010 10.332943 10.837319
Or convert one row DataFrame to Series by DataFrame.squeeze or select first row by position to Series:
my_data.iloc[:, 3:] = my_data.iloc[:, 3:].div(cal_r.squeeze())
#alternative
#my_data.iloc[:, 3:] = my_data.iloc[:, 3:].div(cal_r.iloc[0])
print (my_data)
date type id 1096 1097 1098
0 2014-06-13 cal 1 1.000000 1.000000 1.000000
1 2014-06-13 cow 32 0.029161 -0.052579 -0.098348
2 2014-06-13 cow 47 0.427644 0.401395 0.381012
3 2014-06-13 cow 107 0.231857 0.187632 0.156420
4 2014-06-13 cow 137 0.210654 0.157386 0.124890
5 2014-06-13 cow 255 0.214338 0.154428 0.116671
6 2014-06-13 cow 609 0.339715 0.297749 0.274782
7 2014-06-13 cow 721 0.203523 0.145147 0.105614
8 2014-06-14 cow 817 0.336754 0.303693 0.282788
9 2014-06-14 cow 837 0.537603 0.523857 0.509843
10 2014-06-14 cow 980 0.101236 0.033025 -0.006651
11 2014-06-14 cow 1730 0.474251 0.437866 0.408601
12 2014-06-14 dark 1 9.400010 10.332943 10.837319