If we have the following df,
df
A A B B B
0 10 2 0 3 3
1 20 4 19 21 36
2 30 20 24 24 12
3 40 10 39 23 46
How can I combine the content of the columns with the same names?
e.g.
A B
0 10 0
1 20 19
2 30 24
3 40 39
4 2 3
5 4 21
6 20 24
7 10 23
8 Na 3
9 Na 36
10 Na 12
11 Na 46
I tried groupby and merge and both are not doing this job.
Any help is appreciated.
If columns names are duplicated you can use DataFrame.melt with concat:
df = pd.concat([df['A'].melt()['value'], df['B'].melt()['value']], axis=1, keys=['A','B'])
print (df)
A B
0 10.0 0
1 20.0 19
2 30.0 24
3 40.0 39
4 2.0 3
5 4.0 21
6 20.0 24
7 10.0 23
8 NaN 3
9 NaN 36
10 NaN 12
11 NaN 46
EDIT:
uniq = df.columns.unique()
df = pd.concat([df[c].melt()['value'] for c in uniq], axis=1, keys=uniq)
print (df)
A B
0 10.0 0
1 20.0 19
2 30.0 24
3 40.0 39
4 2.0 3
5 4.0 21
6 20.0 24
7 10.0 23
8 NaN 3
9 NaN 36
10 NaN 12
11 NaN 46
Related
I have this dataframe (df) in python:
Cumulative sales
0 12
1 28
2 56
3 87
I want to create a new column in which I whould have the the number of new sales (N-(N-1)) as below:
Cumulative sales New Sales
0 12 12
1 28 16
2 56 28
3 87 31
You can do
df['new sale']=df.Cumulativesales.diff().fillna(df.Cumulativesales)
df
Cumulativesales new sale
0 12 12.0
1 28 16.0
2 56 28.0
3 87 31.0
Do this:
df['New_sales'] = df['Cumlative_sales'].diff()
df.fillna(df.iloc[0]['Cumlative_sales'], inplace=True)
print(df)
Output:
Cumlative_sales New_sales
0 12 12.0
1 28 16.0
2 56 28.0
3 87 31.0
I am doing kind of research and need to delete the raws containing some values which are not in a specific range using Python.
My Dataset in Excel:
I want to replace the big values of column A (not within range 1-20) with NaN. Replace Big values of column B (not within range 21-40) and so on.
Now I want to drop/ delete the raws contains the NaN values
Expected output should be like:
You can try this to solve your problem. Here, I tried to simulate your problem and solve it with below given code:
import numpy as np
import pandas as pd
data = pd.read_csv('c.csv')
print(data)
data['A'] = data['A'].apply(lambda x: np.nan if x in range(1,10,1) else x)
data['B'] = data['B'].apply(lambda x: np.nan if x in range(10,20,1) else x)
data['C'] = data['C'].apply(lambda x: np.nan if x in range(20,30,1) else x)
print(data)
data = data.dropna()
print(data)
Orignal data:
A B C
0 1 10 20
1 2 11 22
2 4 15 25
3 8 20 30
4 12 25 35
5 18 40 55
6 20 45 60
Output with NaN:
A B C
0 NaN NaN NaN
1 NaN NaN NaN
2 NaN NaN NaN
3 NaN 20.0 30.0
4 12.0 25.0 35.0
5 18.0 40.0 55.0
6 20.0 45.0 60.0
Final Output:
A B C
4 12.0 25.0 35.0
5 18.0 40.0 55.0
6 20.0 45.0 60.0
Try this for non-integer numbers:
import numpy as np
import pandas as pd
data = pd.read_csv('c.csv')
print(data)
data['A'] = data['A'].apply(lambda x: np.nan if x in (round(y,2) for y in np.arange(1.00,10.00,0.01)) else x)
data['B'] = data['B'].apply(lambda x: np.nan if x in (round(y,2) for y in np.arange(10.00,20.00,0.01)) else x)
data['C'] = data['C'].apply(lambda x: np.nan if x in (round(y,2) for y in np.arange(20.00,30.00,0.01)) else x)
print(data)
data = data.dropna()
print(data)
Output:
A B C
0 1.25 10.56 20.11
1 2.39 11.19 22.92
2 4.00 15.65 25.27
3 8.89 20.31 30.15
4 12.15 25.91 35.64
5 18.29 40.15 55.98
6 20.46 45.00 60.48
A B C
0 NaN NaN NaN
1 NaN NaN NaN
2 NaN NaN NaN
3 NaN 20.31 30.15
4 12.15 25.91 35.64
5 18.29 40.15 55.98
6 20.46 45.00 60.48
A B C
4 12.15 25.91 35.64
5 18.29 40.15 55.98
6 20.46 45.00 60.48
try this,
df= df.drop(df.index[df.idxmax()])
O/P:
A B C D
0 1 21 41 61
1 2 22 42 62
2 3 23 43 63
3 4 24 44 64
4 5 25 45 65
5 6 26 46 66
6 7 27 47 67
7 8 28 48 68
8 9 29 49 69
13 14 34 54 74
14 15 35 55 75
15 16 36 56 76
16 17 37 57 77
17 18 38 58 78
18 19 39 59 79
19 20 40 60 80
use idxmax and drop the returned index.
I have a dataframe like below. I would like to sum row 0 to 4 (every 5 rows) and create another column with summed value ("new column"). My real dataframe has 263 rows so, last three rows every 12 rows will be sum of three rows only. How I can do this using Pandas/Python. I have started to learn Python recently. Thanks for any advice in advance!
My data patterns is more complex as I am using the index as one of my column values and it repeats like:
Row Data "new column"
0 5
1 1
2 3
3 3
4 2 14
5 4
6 8
7 1
8 2
9 1 16
10 0
11 2
12 3 5
0 3
1 1
2 2
3 3
4 2 11
5 2
6 6
7 2
8 2
9 1 13
10 1
11 0
12 1 2
...
259 50 89
260 1
261 4
262 5 10
I tried iterrows and groupby but can't make it work so far.
Use this:
df['new col'] = df.groupby(df.index // 5)['Data'].transform('sum')[lambda x: ~(x.duplicated(keep='last'))]
Output:
Data new col
0 5 NaN
1 1 NaN
2 3 NaN
3 3 NaN
4 2 14.0
5 4 NaN
6 8 NaN
7 1 NaN
8 2 NaN
9 1 16.0
Edit to handle updated question:
g = df.groupby(df.Row).cumcount()
df['new col'] = df.groupby([g, df.Row // 5])['Data']\
.transform('sum')[lambda x: ~(x.duplicated(keep='last'))]
Output:
Row Data new col
0 0 5 NaN
1 1 1 NaN
2 2 3 NaN
3 3 3 NaN
4 4 2 14.0
5 5 4 NaN
6 6 8 NaN
7 7 1 NaN
8 8 2 NaN
9 9 1 16.0
10 10 0 NaN
11 11 2 NaN
12 12 3 5.0
13 0 3 NaN
14 1 1 NaN
15 2 2 NaN
16 3 3 NaN
17 4 2 11.0
18 5 2 NaN
19 6 6 NaN
20 7 2 NaN
21 8 2 NaN
22 9 1 13.0
23 10 1 NaN
24 11 0 NaN
25 12 1 2.0
HID gen views
1 1 20
1 2 2532
1 3 276
1 4 1684
1 5 779
1 6 200
1 7 545
2 1 20
2 2 7478
2 3 750
2 4 7742
2 5 2643
2 6 208
2 7 585
3 1 21
3 2 4012
3 3 2019
3 4 1073
3 5 3372
3 6 8
3 7 1823
3 8 22
this is a sample section of a data frame, where HID and gen are indexes.
how can it be transformed like this
HID 1 2 3 4 5 6 7 8
1 20 2532 276 1684 779 200 545 nan
2 20 7478 750 7742 2643 208 585 nan
3 21 4012 2019 1073 3372 8 1823 22
Its called pivoting i.e
df.reset_index().pivot('HID','gen','views')
gen 1 2 3 4 5 6 7 8
HID
1 20.0 2532.0 276.0 1684.0 779.0 200.0 545.0 NaN
2 20.0 7478.0 750.0 7742.0 2643.0 208.0 585.0 NaN
3 21.0 4012.0 2019.0 1073.0 3372.0 8.0 1823.0 22.0
Use unstack:
df = df['views'].unstack()
If need also HID column add reset_index + rename_axis:
df = df['views'].unstack().reset_index().rename_axis(None, 1)
print (df)
HID 1 2 3 4 5 6 7 8
0 1 20.0 2532.0 276.0 1684.0 779.0 200.0 545.0 NaN
1 2 20.0 7478.0 750.0 7742.0 2643.0 208.0 585.0 NaN
2 3 21.0 4012.0 2019.0 1073.0 3372.0 8.0 1823.0 22.0
I have a large DataFrame which is indexed by datetime, in particular, by days. I am looking for an efficient function which, for each column, checks the most common non-null value in each week, and outputs a dataframe which is indexed by weeks consisting of these within-week most common values.
Here is an example. The following DataFrame consists of two weeks of daily data:
0 1
2015-11-12 00:00:00 8 nan
2015-11-13 00:00:00 7 nan
2015-11-14 00:00:00 nan 5
2015-11-15 00:00:00 7 nan
2015-11-16 00:00:00 8 nan
2015-11-17 00:00:00 7 nan
2015-11-18 00:00:00 5 nan
2015-11-19 00:00:00 9 nan
2015-11-20 00:00:00 8 nan
2015-11-21 00:00:00 6 nan
2015-11-22 00:00:00 6 nan
2015-11-23 00:00:00 6 nan
2015-11-24 00:00:00 6 nan
2015-11-25 00:00:00 2 nan
and should be transformed into:
0 1
2015-11-12 00:00:00 7 5
2015-11-19 00:00:00 6 nan
My DataFrame is very large so efficiency is important. Thanks.
EDIT: If possible, can someone suggest a method that would be applicable if the entries are tuples (instead of floats as in my example)?
You can use resample to group your data by the weekly interval. Then, count the number of occurences via pd.value_counts and select the most common with idxmax:
df.resample("7D").apply(lambda x: x.apply(pd.value_counts).idxmax())
0 1
2015-11-12 00:00:00 7.0 5.0
2015-11-19 00:00:00 6.0 NaN
Edit
Here is another numpy version which is faster than the above solution:
def numpy_mode(series):
values = series.values
dropped = values[~np.isnan(values)]
# check for empty array and return NaN
if not dropped.size:
return np.NaN
uniques, counts = np.unique(series.dropna(), return_counts=True)
return uniques[np.argmax(counts)]
df2.resample("7D").apply(lambda x: x.apply(get_mode))
0 1
2015-11-12 00:00:00 7.0 5.0
2015-11-19 00:00:00 6.0 NaN
And here the timings based on the dummy data (for further improvements, have a look here):
%%timeit
df2.resample("7D").apply(lambda x: x.apply(pd.value_counts).idxmax())
>>> 100 loops, best of 3: 18.6 ms per loop
%%timeit
df2.resample("7D").apply(lambda x: x.apply(get_mode))
>>> 100 loops, best of 3: 3.72 ms per loop
I also tried scipy.stats.mode however it was also slower than the numpy solution:
size = 1000
index = pd.DatetimeIndex(start="2012-12-12", periods=size, freq="D")
dummy = pd.DataFrame(np.random.randint(0, 20, size=(size, 50)), index=index)
print(dummy.head)
0 1 2 3 4 5 6 7 8 9 ... 40 41 42 43 44 45 46 47 48 49
2012-12-12 18 2 7 1 7 9 16 2 19 19 ... 10 2 18 16 15 10 7 19 9 6
2012-12-13 7 4 11 19 17 10 18 0 10 7 ... 19 11 5 5 11 4 0 16 12 19
2012-12-14 14 0 14 5 1 11 2 19 5 9 ... 2 9 4 2 9 5 19 2 16 2
2012-12-15 12 2 7 2 12 12 11 11 19 5 ... 16 0 4 9 13 5 10 2 14 4
2012-12-16 8 15 2 18 3 16 15 0 14 14 ... 18 2 6 13 19 10 3 16 11 4
%%timeit
dummy.resample("7D").apply(lambda x: x.apply(get_mode))
>>> 1 loop, best of 3: 926 ms per loop
%%timeit
dummy.resample("7D").apply(lambda x: x.apply(pd.value_counts).idxmax())
>>> 1 loop, best of 3: 5.84 s per loop
%%timeit
dummy.resample("7D").apply(lambda x: stats.mode(x).mode)
>>> 1 loop, best of 3: 1.32 s per loop