I have a dataframe where one column has strings that sometimes contain a word and parentheses around the value I want to keep. How do I do remove them? Here's what I have:
import pandas as pd
df = pd.read_csv("Espacios_#cronista.csv")
del df['Espacio']
df[df['Tamano'].str.contains("Variable")]
Output I have:
Tamano Subastas Imp Fill_rate
0 Variable (300x600) 43 13 5.99
1 Variable (266x600) 43 5 4.44
2 266x600 43 5 4.44
Output I need:
Tamano Subastas Imp Fill_rate
0 300x600 43 13 5.99
1 266x600 43 5 4.44
2 266x600 43 5 4.44
This is a good use case for pd.Series.str.extract
pipelined
Meaning, assign creates a copy. You can use fillna to fill in spots that became NaN.
pat = 'Variable\s*\((.*)\)'
df.assign(Tamano=df.Tamano.str.extract(pat, expand=False).fillna(df.Tamano))
Tamano Subastas Imp Fill_rate
0 300x600 43 13 5.99
1 266x600 43 5 4.44
2 266x600 43 5 4.44
in place
Meaning we alter df
pat = 'Variable\s*\((.*)\)'
df.update(df.Tamano.str.extract(pat, expand=False))
df
Tamano Subastas Imp Fill_rate
0 300x600 43 13 5.99
1 266x600 43 5 4.44
2 266x600 43 5 4.44
IIUC, this should work
cond = df.Tamano.str.contains("Variable")
df.loc[cond, "Tamano"] = df.Tamano.str.extract("((?<=\()[^)]*)", expand=False)
Tamano Subastas Imp Fill_rate
0 300x600 43 13 5.99
1 266x600 43 5 4.44
2 266x600 43 5 4.44
This will select the rows fit the condition: df.Tamano.str.contains("Variable") to do replacement. The regular expression (?<=\() means will look for ( and match what is behind. The matching criterion [^)]* is to match any that is not ), and thus will stop when meeting a ). piRSquared's regular expression is more simple and easy to understand.
Related
The data I used look like this
data
Subject 2000_X1 2000_X2 2001_X1 2001_X2 2002_X1 2002_X2
1 100 50 120 45 110 50
2 95 40 100 45 105 50
3 110 45 100 45 110 40
I want to calculate each variable growth for each year so the result will look like this
Subject 2001_X1_gro 2001_X2_gro 2002_X1_gro 2002_X2_gro
1 0.2 -0.1 -0.08333 0.11111
2 0.052632 0.125 0.05 0.11111
3 -0.09091 0 0.1 -0.11111
I already do it manually for each variable for each year with code like this
data[2001_X1_gro]= (data[2001_X1]-data[2000_X1])/data[2000_X1]
data[2002_X1_gro]= (data[2002_X1]-data[2001_X1])/data[2001_X1]
data[2001_X2_gro]= (data[2001_X2]-data[2000_X2])/data[2000_X2]
data[2002_X2_gro]= (data[2002_X2]-data[2001_X2])/data[2001_X2]
Is there a way to do it more efficient escpecially if I have more year and/or more variable?
import pandas as pd
df = pd.read_csv('data.txt', sep=',', header=0)
Input
Subject 2000_X1 2000_X2 2001_X1 2001_X2 2002_X1 2002_X2
0 1 100 50 120 45 110 50
1 2 95 40 100 45 105 50
2 3 110 45 100 45 110 40
Next, a loop is created and the columns are filled:
qqq = '_gro'
new_name = ''
year = ''
for i in range(1, len(df.columns) - 2):
year = str(int(df.columns[i][:4]) + 1) + df.columns[i][4:]
new_name = year + qqq
df[new_name] = (df[year] - df[df.columns[i]])/df[df.columns[i]]
print(df)
Output
Subject 2000_X1 2000_X2 2001_X1 2001_X2 2002_X1 2002_X2 2001_X1_gro \
0 1 100 50 120 45 110 50 0.200000
1 2 95 40 100 45 105 50 0.052632
2 3 110 45 100 45 110 40 -0.090909
2001_X2_gro 2002_X1_gro 2002_X2_gro
0 -0.100 -0.083333 0.111111
1 0.125 0.050000 0.111111
2 0.000 0.100000 -0.111111
In the loop, the year is extracted from the column name, converted to int, 1 is added to it. The value is again converted to a string, the prefix '_Xn' is added. A new_name variable is created, to which the string '_gro ' is also appended. A column is created and filled with calculated values.
If you want to count, for example, for three years, then you need to add not 1, but 3. This is with the condition that your data will be ordered. And note that the loop does not go through all the elements: for i in range(1, len(df.columns) - 2):. In this case, it skips the Subject column and stops short of the last two values. That is, you need to know where to stop it.
As part of a larger task, I want to calculate the monthly mean values for each specific station. This is already difficult to do, but I am getting close.
The dataframe has many columns, but ultimately I only use the following information:
Date Value Station_Name
0 2006-01-03 18 2
1 2006-01-04 12 2
2 2006-01-05 11 2
3 2006-01-06 10 2
4 2006-01-09 22 2
... ... ...
3510 2006-12-23 47 45
3511 2006-12-24 46 45
3512 2006-12-26 35 45
3513 2006-12-27 35 45
3514 2006-12-30 28 45
I am running into two issues, using:
df.groupby(['Station_Name', pd.Grouper(freq='M')])['Value'].mean()
It results in something like:
Station_Name Date
2 2003-01-31 29.448387
2003-02-28 30.617857
2003-03-31 28.758065
2003-04-30 28.392593
2003-05-31 30.318519
...
45 2003-09-30 16.160000
2003-10-31 18.906452
2003-11-30 26.296667
2003-12-31 30.306667
2004-01-31 29.330000
Which I can't seem to use as a regular dataframe, and the datetime is messed up as it doesn't show the monthly mean but gives the last day back. Also the station name is a single index, and not for the whole column. Plus the mean value doesn't have a "column name" at all. This isn't a dataframe, but a pandas.core.series.Series. I can't convert this again because it's not correct, and using the .to_frame() method shows that it is still indeed a Dataframe. I don't get this part.
I found that in order to return a normal dataframe, to use
as_index = False
In the groupby method. But this results in the months not being shown:
df.groupby(['station_name', pd.Grouper(freq='M')], as_index = False)['Value'].mean()
Gives:
Station_Name Value
0 2 29.448387
1 2 30.617857
2 2 28.758065
3 2 28.392593
4 2 30.318519
... ... ...
142 45 16.160000
143 45 18.906452
144 45 26.296667
145 45 30.306667
146 45 29.330000
I can't just simply add the month later, as not every station has an observation in every month.
I've tried using other methods, such as
df.resample("M").mean()
But it doesn't seem possible to do this on multiple columns. It returns the mean value of everything.
Edit: This is ultimately what I would want.
Station_Name Date Value
0 2 2003-01 29.448387
1 2 2003-02 30.617857
2 2 2003-03 28.758065
3 2 2003-04 28.392593
4 2 2003-05 30.318519
... ... ...
142 45 2003-08 16.160000
143 45 2003-09 18.906452
144 45 2003-10 26.296667
145 45 2003-11 30.306667
146 45 2003-12 29.330000
ok , how baout this :
df = df.groupby(['Station_Name',df['Date'].dt.to_period('M')])['Value'].mean().reset_index()
outut:
>>
Station_Name Date Value
0 2 2006-01 14.6
1 45 2006-12 38.2
I have the following two dataframes, which have different size: df1 (966 rows x 2 cols), df2 (36 rows, 2 cols), where
df1:
Video_# Selected Joint.1
484 1 Left_shoulder
778 1 Left_shoulder
418 1 Right_shoulder
964 1 Right_shoulder
193 1 Right_shoulder
... ... ... ... ...
285 36 Right_elbow
267 36 Left_hand
216 36 Shoulder_centre
139 36 Right_shoulder
df2:
Video_# Ann.1
0 1 Shoulder_center
1 2 Head
2 3 Right_hip
... ... ... ... ...
33 34 Left_knee
34 35 Right_knee
35 36 Right_shoulder
Where Video_# goes from 1-36. In df2 the Video_# column is just a single occurrence of each, so 1-36 just once. Whereas in df1 there are multiple occurrences of each 1-36, not the same number for each 1-36 (I hope that makes sense).
What I want to check is the number of occurrences df1['Selected Joint.1'] == df2['Ann.1'] based on Video_#. So the expected output is (eg.):
Video_# Equality Occurrences
1 3
2 5
... ... ...
36 6
Is that possible?
Use DataFrame.merge with GroupBy.size:
df = (df1.merge(df2,
left_on=['Video_#','Selected Joint.1'],
right_on=['Video_#','Ann.1'])
.groupby('Video_')
.size()
.reset_index(name='Equality Occurrences'))
Suppose, you have a column in excel, with values like this... there are only 5500 numbers present but it show length 5602 means that 102 strings are present
4 SELECTIO
6 N NO
14 37001
26 37002
38 37003
47 37004
60 37005
73 37006
82 37007
92 37008
105 37009
119 37010
132 37011
143 37012
157 37013
168 37014
184 37015
196 37016
207 37017
220 37018
236 37019
253 37020
267 37021
280 37022
287 Krishan
290 37023
300 37024
316 37025
337 37026
365 37027
...
74141 42471
74154 42472
74169 42473
74184 42474
74200 42475
74216 42476
74233 42477
74242 42478
74256 42479
74271 42480
74290 42481
74309 42482
74323 42483
74336 42484
74350 42485
74365 42486
74378 42487
74389 42488
74398 42489
74413 42490
74430 42491
74446 42492
74459 42493
74474 42494
74491 42495
74504 42496
74516 42497
74530 42498
74544 42499
74558 42500
Name: Selection No., Length: 5602, dtype: object
and I want to get only numeric values like this in python using pandas
37001
37002
37003
37004
37005
how can I do this? I have attached my code in python using pandas..............................................
def selection(sle):
if sle in re.match('[3-4][0-9]{4}',sle):
return 1
else:
return 0
select['status'] = select['Selection No.'].apply(selection)
and now I am geting an "argument of type 'NoneType' is not iterable" error.
Try using Numpy with np.isreal and only select numbers..
import pandas as pd
import numpy as np
df = pd.DataFrame({'SELECTIO':['N NO',37002,37003,'Krishan',37004,'singh',37005], 'some_col':[4,6,14,26,38,47,60]})
df
SELECTIO some_col
0 N NO 4
1 37002 6
2 37003 14
3 Krishan 26
4 37004 38
5 singh 47
6 37005 60
>>> df[df[['SELECTIO']].applymap(np.isreal).all(1)]
SELECTIO some_col
1 37002 6
2 37003 14
4 37004 38
6 37005 60
result:
Specific to column SELECTIO ..
df[df[['SELECTIO']].applymap(np.isreal).all(1)]
SELECTIO some_col
1 37002 6
2 37003 14
4 37004 38
6 37005 60
OR just another approach importing numbers + lambda :
import numbers
df[df[['SELECTIO']].applymap(lambda x: isinstance(x, numbers.Number)).all(1)]
SELECTIO some_col
1 37002 6
2 37003 14
4 37004 38
6 37005 60
Note: there is problem when you are extracting a column you are using ['Selection No.'] but indeed you have a Space in the name it will be like ['Selection No. '] that's the reason you are getting KeyError while executing it, try and see!
Your function contains wrong expression: if sle in re.match('[3-4][0-9]{4}',sle): - it tries to find a column value sle IN match object which "always have a boolean value of True" (re.match returns None when there's no match)
I would suggest to proceed with pd.Series.str.isnumeric function:
In [544]: df
Out[544]:
Selection No.
0 37001
1 37002
2 37003
3 asnsh
4 37004
5 singh
6 37005
In [545]: df['Status'] = df['Selection No.'].str.isnumeric().astype(int)
In [546]: df
Out[546]:
Selection No. Status
0 37001 1
1 37002 1
2 37003 1
3 asnsh 0
4 37004 1
5 singh 0
6 37005 1
If a strict regex pattern is required - use pd.Series.str.contains function:
df['Status'] = df['Selection No.'].str.contains('^[3-4][0-9]{4}$', regex=True).astype(int)
I have looked through a lot of posts, but none of the solutions I can implement in my code:
x4 = x4.set_index('grupa').T.rename_axis('DANE').reset_index().rename_axis(None,1).round()
After which I get the results DataFrame:
DANE BAKALIE NASIONA OWOCE WARZYWA
0 ilosc 5.0 94.0 61.0 623.0
1 marza_netto 7.0 120.0 69.0 668.0
2 marza_procent2 32.0 34.0 29.0 27.0
But I would like to receive:
DANE BAKALIE NASIONA OWOCE WARZYWA
0 ilosc 5 94 61 623
1 marza_netto 7 120 69 668
2 marza_procent2 32 34 29 27
I tried replace('.0',''),int(round(),astype(int), but I don't get good results or I get the incompatibility of the attributes with the DataFrame.
If only non numeric column is DANE then cast before convert to column:
x4 = x4.set_index('grupa')
.T
.rename_axis('DANE')
.astype(int)
.reset_index()
.rename_axis(None,1)
More general solution is select all floats columns and cast:
cols = df.select_dtypes(include=['float']).columns
df[cols] = df[cols].astype(int)
print (df)
DANE BAKALIE NASIONA OWOCE WARZYWA
0 ilosc 5 94 61 623
1 marza_netto 7 120 69 668
2 marza_procent2 32 34 29 27
If some NaNs values convert to int is not possible.
So is possible:
1.drop all NaNs rows:
df = df.dropna()
2.replace NaNs to some integer like 0:
df = df.fillna(0)
Not 100% sure I got your question, but you can use an astype(int) conversion.
df = df.set_index('DANE').astype(int).reset_index()
df
DANE BAKALIE NASIONA OWOCE WARZYWA
0 ilosc 5 94 61 623
1 marza_netto 7 120 69 668
2 marza_procent2 32 34 29 27
If you're dealing with rows that have NaNs, either drop those rows and convert, or convert to astype(object). The latter is not recommended because you lose performance.