I have the following dataset stored in CSV file:
Name Salary
John 50000
Eric 150000
Paul 100000
I want to get the median salary and output this value in the SAME csv file as follows:
Name Salary Median
John 50000 100000
Eric 150000 100000
Paul 100000 100000
Code:
import numpy as np
from numpy import genfromtxt
dataset = genfromtxt('C:\\Users\abc.csv',delimiter=',')
x=dataset[2:,0]
y=np.median(x)
Now that you have x and y, you can just dump them with np.savetxt.
np.savetxt('C:\\Users\abc.csv', np.hstack((x[2:], y.reshape(-1, 1))) , delimiter=',')
try pandas,In your case:
>>>files = 'example.csv'
>>>df = pd.read_csv(files)
>>>df = df.convert_objects(convert_numeric=True)
>>>df['Median'] = df['Salary'].median()
>>>df.to_csv(files)
more explanation
>>>files = 'example.csv'
>>>df = pd.read_csv(files)
>>>df = df.convert_objects(convert_numeric=True)
>>>df
A B C D
0 1 2 3 4
1 5 6 7 8
2 9 10 11 12
df['new']=13
>>>df
A B C D new
0 1 2 3 4 13
1 5 6 7 8 13
2 9 10 11 12 13
>>>df['new'] = df['new'] +1
>>>df
A B C D new
0 1 2 3 4 14
1 5 6 7 8 14
2 9 10 11 12 14
>>>df.insert(2,'win',22)
>>>df
A B win C D new
0 1 2 22 3 4 14
1 5 6 22 7 8 14
2 9 10 22 11 12 14
Related
I have a dataframe with stores and its invoices numbers and I need to find the missing consecutive invoices numbers per Store, for example:
df1 = pd.DataFrame()
df1['Store'] = ['A','A','A','A','A','B','B','B','B','C','C','C','D','D']
df1['Invoice'] = ['1','2','5','6','8','20','23','24','30','200','202','203','204','206']
Store Invoice
0 A 1
1 A 2
2 A 5
3 A 6
4 A 8
5 B 20
6 B 23
7 B 24
8 B 30
9 C 200
10 C 202
11 C 203
12 D 204
13 D 206
And I want a dataframe like this:
Store MissInvoice
0 A 3
1 A 4
2 A 7
3 B 21
4 B 22
5 B 25
6 B 26
7 B 27
8 B 28
9 B 29
10 C 201
11 D 205
Thanks in advance!
You can use groupby.apply to compute a set difference with the range from the min to max value. Then explode:
(df1.astype({'Invoice': int})
.groupby('Store')['Invoice']
.apply(lambda s: set(range(s.min(), s.max())).difference(s))
.explode().reset_index()
)
NB. if you want to ensure having sorted values, use lambda s: sorted(set(range(s.min(), s.max())).difference(s)).
Output:
Store Invoice
0 A 3
1 A 4
2 A 7
3 B 21
4 B 22
5 B 25
6 B 26
7 B 27
8 B 28
9 B 29
10 C 201
11 D 205
Here's an approach:
import pandas as pd
import numpy as np
df1 = pd.DataFrame()
df1['Store'] = ['A','A','A','A','A','B','B','B','B','C','C','C']
df1['Invoice'] = ['1','2','5','6','8','20','23','24','30','200','202','203']
df1['Invoice'] = df1['Invoice'].astype(int)
df2 = df1.groupby('Store')['Invoice'].agg(['min','max'])
df2['MissInvoice'] = [[]]*len(df2)
for store,row in df2.iterrows():
df2.at[store,'MissInvoice'] = np.setdiff1d(np.arange(row['min'],row['max']+1),
df1.loc[df1['Store'] == store, 'Invoice'])
df2 = df2.explode('MissInvoice').drop(columns = ['min','max']).reset_index()
The resulting dataframe df2:
Store MissInvoice
0 A 3
1 A 4
2 A 7
3 B 21
4 B 22
5 B 25
6 B 26
7 B 27
8 B 28
9 B 29
10 C 201
Note: Store D is absent from the dataframe in my code because it is omitted from the lines in the question defining df1.
dataframe
df.columns=['ipo_date','l2y_gg_date','l1k_kk_date']
Goal
return dataframe with columns name containing _date except for ipo_date.
Try
df.filter(regex='_date&^ipo_date')
Try a negative lookbehind:
import numpy as np
import pandas as pd
df = pd.DataFrame(np.arange(1, 21).reshape((5, 4)),
columns=['ipo_date', 'l2y_gg_date', 'l1k_kk_date', 'other'])
filtered = df.filter(regex=r'(?<!ipo)_date')
print(filtered)
Sample df:
ipo_date l2y_gg_date l1k_kk_date other
0 1 2 3 4
1 5 6 7 8
2 9 10 11 12
3 13 14 15 16
4 17 18 19 20
filtered:
l2y_gg_date l1k_kk_date
0 2 3
1 6 7
2 10 11
3 14 15
4 18 19
I am having trouble applying some logic across my entire dataset. I am able to apply the logic on a small "group" but not on all of the groups (note, the groups are made by primaryFilter and secondaryFilter. Do you all mind pointing me in the right direction to go about this?
Entire Data
import pandas as pd
import numpy as np
myInput = {
'primaryFilter': [100,100,100,100,100,100,100,100,100,100,200,200,200,200,200,200,200,200,200,200],
'secondaryFilter': [1,1,1,1,2,2,2,3,3,3,1,1,2,2,2,2,3,3,3,3],
'constantValuePerGroup': [15,15,15,15,20,20,20,17,17,17,10,10,30,30,30,30,22,22,22,22],
'someValue':[3,1,4,7,9,9,2,7,3,7,6,4,7,10,10,3,4,6,7,5]
}
df_input = pd.DataFrame(data=myInput)
df_input
Test Data (First Group)
df_test = df_input[df_input.primaryFilter.isin([100])]
df_test = df_test[df_test.secondaryFilter == 1.0]
df_test['newColumn'] = np.nan
for index,row in df_test.iterrows():
if index==0:
print("start")
df_test.loc[0, 'newColumn'] = 0
elif index==df_test.shape[0]-1:
df_test.loc[index, 'newColumn'] = df_test.loc[index-1, 'newColumn'] + df_test.loc[index-1, 'someValue']
print("end")
else:
print("inter")
df_test.loc[index, 'newColumn'] = df_test.loc[index-1, 'newColumn'] + df_test.loc[index-1, 'someValue']
df_test["delta"] = df_test["constantValuePerGroup"] - df_test['newColumn']
df_test.head()
Here is the output of the test
I now would like to apply the above logic to the remaining groups 100,2 and 100,3 and 200,1 and so forth..
No need to use iterrows here, you can group the dataframe on primaryFilter and secondaryFilter columns then for each unique group take the cumulative sum of values in column someValue and shift the resulting cummulative sum by 1 position downwards to obtain newColumn. Finally subtract newColumn from constantValuePerGroup to get the delta.
df_input['newColumn'] = df_input.groupby(['primaryFilter', 'secondaryFilter'])['someValue'].apply(lambda s: s.cumsum().shift(fill_value=0))
df_input['delta'] = df_input['constantValuePerGroup'] - df_input['newColumn']
>>> df_input
primaryFilter secondaryFilter constantValuePerGroup someValue newColumn delta
0 100 1 15 3 0 15
1 100 1 15 1 3 12
2 100 1 15 4 4 11
3 100 1 15 7 8 7
4 100 2 20 9 0 20
5 100 2 20 9 9 11
6 100 2 20 2 18 2
7 100 3 17 7 0 17
8 100 3 17 3 7 10
9 100 3 17 7 10 7
10 200 1 10 6 0 10
11 200 1 10 4 6 4
12 200 2 30 7 0 30
13 200 2 30 10 7 23
14 200 2 30 10 17 13
15 200 2 30 3 27 3
16 200 3 22 4 0 22
17 200 3 22 6 4 18
18 200 3 22 7 10 12
19 200 3 22 5 17 5
When I have a below df, I want to get a column 'C' which has max value between specific value '15' and column 'A' within the condition "B == 't'"
testdf = pd.DataFrame({"A":[20, 16, 7, 3, 8],"B":['t','t','t','t','f']})
testdf
A B
0 20 t
1 16 t
2 7 t
3 3 t
4 8 f
I tried this:
testdf.loc[testdf['B']=='t', 'C'] = max(15,(testdf.loc[testdf['B']=='t','A']))
And desired output is:
A B C
0 20 t 20
1 16 t 16
2 7 t 15
3 3 t 15
4 8 f 8
Could you help me to get the output? Thank you!
Use np.where with clip:
testdf['C'] = np.where(testdf['B'].eq('t'),
testdf['A'].clip(15), df['A'])
Or similarly with series.where:
testdf['C'] = (testdf['A'].clip(15)
.where(testdf['B'].eq('t'), testdf['A'])
)
output:
A B C
0 20 t 20
1 16 t 16
2 7 t 15
3 3 t 15
4 8 f 8
You could also use the update method:
testdf['C'] = testdf['A']
A B C
0 20 t 20
1 16 t 16
2 7 t 7
3 3 t 3
4 8 f 8
values = testdf.A[testdf.B.eq('t')].clip(15)
values
Out[16]:
0 20
1 16
2 15
3 15
Name: A, dtype: int64
testdf.update(values.rename('C'))
A B C
0 20 t 20.0
1 16 t 16.0
2 7 t 15.0
3 3 t 15.0
4 8 f 8.0
To apply any formula to individual values in a dataframe you can use
df['column'] =df['column'].apply(lambda x: anyFunc(x))
x here will catch individual values of column one by one and pass it to the function where you can manipulate it and return back.
I have a pandas DataFrame say this:
user value
0 a 1
1 a 2
2 a 3
3 a 4
4 a 5
5 b 6
6 b 7
7 b 8
8 b 9
9 b 10
10 c 11
11 c 12
12 c 13
13 c 14
14 c 15
Now I want to group by user, and create two mutually exclusive random samples out of it e.g
Set1 with 1 samples per group:
user value
3 a 4
9 b 10
13 c 14
Set2 with 2 samples per group:
user value
0 a 1
1 a 2
5 b 6
6 b 7
10 c 11
11 c 12
So far i'v tried this:
u = np.array(['a','b','c'])
u = np.repeat(u,5)
df = pd.DataFrame({'user':u,'value':[1,2,3,4,5,6,7,8,9,10,11,12,13,14,15]})
set1 = df.groupby(['user']).tail(1)
set2 = df.groupby(['user']).head(2)
But these are not random samples, and i would like them to be mutually exclusive. Any ideas?
PS. Each group always has at least 3 elements
You can randomly select 3 records for each user:
a = df.groupby("user")["value"].apply(lambda x: x.sample(3))
a
Out[27]:
user
a 3 4
0 1
2 3
b 5 6
7 8
6 7
c 14 15
10 11
13 14
dtype: int64
And assign first one to the first set, the remaining two to the second set:
a.groupby(level=0).head(1)
Out[28]:
user
a 3 4
b 5 6
c 14 15
dtype: int64
a.groupby(level=0).tail(2)
Out[29]:
user
a 0 1
2 3
b 7 8
6 7
c 10 11
13 14
dtype: int64
This maybe a bit naive but all I did was reindex the DataFrame with a random permutation of the length of the DataFrame and reset the index. After that I take the head and tail as you did with your original code, seems to work. This could probably be made into a function:
a = np.arange(len(df))
np.random.shuffle(a)
df = df.reindex(a).reset_index()
set1 = df.groupby(['user']).tail(1)
>>>
index user value
12 9 b 10
13 10 c 11
14 1 a 2
set2 = df.groupby(['user']).head(2)
>>>
index user value
0 6 b 7
1 2 a 3
2 5 b 6
3 13 c 14
4 3 a 4
6 12 c 13
Hope this helps.
There is likely a better solution but what about just randomizing your data before grouping and then taking the tail and head per group? You could take a set of your indices, take a random permutation of it and use that to create a new scrambled dataframe, then do your current procedure.