Develop Reference

Pandas Multiply 2D by 1D Dataframe

Looking for an elegant way to multiply a 2D dataframe by a 1D series where the indices and column names align
df1 =
Index
A
B
1
1
5
2
2
6
3
3
7
4
4
8
df2 =
Coef
A
10
B
100
Something like...
df3 = df1.mul(df2)
To get :
Index
A
B
1
10
500
2
20
600
3
30
700
4
40
800

There is no such thing as 1D DataFrame, you need to slice as Series to have 1D, then multiply (by default on axis=1):
df3 = df1.mul(df2['Coef'])
Output:
A B
1 10 500
2 20 600
3 30 700
4 40 800
If Index is a column:
df3 = df1.mul(df2['Coef']).combine_first(df1)[df1.columns]
Output:
Index A B
0 1.0 10.0 500.0
1 2.0 20.0 600.0
2 3.0 30.0 700.0
3 4.0 40.0 800.0

Drop rows after maximum value in a grouped Pandas dataframe

I've got a date-ordered dataframe that can be grouped. What I am attempting to do is groupby a variable (Person), determine the maximum (weight) for each group (person), and then drop all rows that come after (date) the maximum.
Here's an example of the data:
df = pd.DataFrame({'Person': 1,1,1,1,1,2,2,2,2,2],'Date': '1/1/2015','2/1/2015','3/1/2015','4/1/2015','5/1/2015','6/1/2011','7/1/2011','8/1/2011','9/1/2011','10/1/2011'], 'MonthNo':[1,2,3,4,5,1,2,3,4,5], 'Weight':[100,110,115,112,108,205,210,211,215,206]})
Date MonthNo Person Weight
0 1/1/2015 1 1 100
1 2/1/2015 2 1 110
2 3/1/2015 3 1 115
3 4/1/2015 4 1 112
4 5/1/2015 5 1 108
5 6/1/2011 1 2 205
6 7/1/2011 2 2 210
7 8/1/2011 3 2 211
8 9/1/2011 4 2 215
9 10/1/2011 5 2 206
Here's what I want the result to look like:
Date MonthNo Person Weight
0 1/1/2015 1 1 100
1 2/1/2015 2 1 110
2 3/1/2015 3 1 115
5 6/1/2011 1 2 205
6 7/1/2011 2 2 210
7 8/1/2011 3 2 211
8 9/1/2011 4 2 215
I think its worth noting, there can be disjoint start dates and the maximum may appear at different times.
My idea was to find the maximum for each group, obtain the MonthNo the maximum was in for that group, and then discard any rows with MonthNo greater Max Weight MonthNo. So far I've been able to obtain the max by group, but cannot get past doing a comparison based on that.
Please let me know if I can edit/provide more information, haven't posted many questions here! Thanks for the help, sorry if my formatting/question isn't clear.

Using idxmax with groupby
df.groupby('Person',sort=False).apply(lambda x : x.reset_index(drop=True).iloc[:x.reset_index(drop=True).Weight.idxmax()+1,:])
Out[131]:
Date MonthNo Person Weight
Person
1 0 1/1/2015 1 1 100
1 2/1/2015 2 1 110
2 3/1/2015 3 1 115
2 0 6/1/2011 1 2 205
1 7/1/2011 2 2 210
2 8/1/2011 3 2 211
3 9/1/2011 4 2 215

You can use groupby.transform with idxmax. The first 2 steps may not be necessary depending on how your dataframe is structured.
# convert Date to datetime
df['Date'] = pd.to_datetime(df['Date'])
# sort by Person and Date to make index usable for next step
df = df.sort_values(['Person', 'Date']).reset_index(drop=True)
# filter for index less than idxmax transformed by group
df = df[df.index <= df.groupby('Person')['Weight'].transform('idxmax')]
print(df)
Date MonthNo Person Weight
0 2015-01-01 1 1 100
1 2015-02-01 2 1 110
2 2015-03-01 3 1 115
5 2011-06-01 1 2 205
6 2011-07-01 2 2 210
7 2011-08-01 3 2 211
8 2011-09-01 4 2 215

Pandas: Add new column based on comparison of two DFs

I have 2 dataframes that I am wanting to compare one to the other and add a 'True/False' to a new column in the first based on the comparison.
My data resembles:
DF1:
cat sub-cat low high
3 3 1 208 223
4 3 1 224 350
8 4 1 223 244
9 4 1 245 350
13 5 1 232 252
14 5 1 253 350
DF2:
Cat Sub-Cat Rating
0 5 1 246
1 5 2 239
2 8 1 203
3 8 2 218
4 K 1 149
5 K 2 165
6 K 1 171
7 K 2 185
8 K 1 157
9 K 2 171
Desired result would be for DF2 to have an additional column with a True or False depending on if, based on the cat and sub-cat, that the rating is between the low.min() and high.max() or Null if no matches found to compare.
Have been running rounds with this for far too long with no results to speak of.
Thank you in advance for any assistance.
Update:
First row would look something like:
Cat Sub-Cat Rating In-Spec
0 5 1 246 True
As it falls within the min low and the max high.
Example: There are two rows in DF1 for cat = 5 and sub-cat = 2. I need to get the minimum low and the maximum high from those 2 rows and then check if the rating from row 0 in DF2 falls within the minimum low and maximum high from the two matching rows in DF1

join post groupby.agg
d2 = DF2.join(
DF1.groupby(
['cat', 'sub-cat']
).agg(dict(low='min', high='max')),
on=['Cat', 'Sub-Cat']
)
d2
Cat Sub-Cat Rating high low
0 5 1 246 350.0 232.0
1 5 2 239 NaN NaN
2 8 1 203 NaN NaN
3 8 2 218 NaN NaN
4 K 1 149 NaN NaN
5 K 2 165 NaN NaN
6 K 1 171 NaN NaN
7 K 2 185 NaN NaN
8 K 1 157 NaN NaN
9 K 2 171 NaN NaN
assign with .loc
DF2.loc[d2.eval('low <= Rating <= high'), 'In-Spec'] = True
DF2
Cat Sub-Cat Rating In-Spec
0 5 1 246 True
1 5 2 239 NaN
2 8 1 203 NaN
3 8 2 218 NaN
4 K 1 149 NaN
5 K 2 165 NaN
6 K 1 171 NaN
7 K 2 185 NaN
8 K 1 157 NaN
9 K 2 171 NaN

To add a new column based on a boolean expression would involve something along the lines of:
temp = boolean code involving inequality
df2['new column name'] = temp
However I'm not sure I understand, the first row in your DF2 table for instance, has a rating of 246, which means it's true for row 13 of DF1, but false for row 14. What would you like it to return?

You can do it like this
df2['In-Spec'] = 'False'
df2['In-Spec'][(df2['Rating'] > df1['low']) & (df2['Rating'] < df1['high'])] = 'True'
But which rows should be compared with each others? Do you want them to compare by their index or by their cat & subcat names?

Counting number of ratings without a loop python

In python, given a list of ratings as:
import pandas as pd
path = 'ratings_ml100k.csv'
data = pd.read_csv(path,sep= ',')
print(data)
user_id item_id rating
28422 100 690 4
32020 441 751 4
15819 145 265 5
where the items are:
print(itemsTrain)
[ 690 751 265 ..., 1650 1447 1507]
For each item, I would like to compute the number of ratings. Is there anyway to do this without resorting to a Loop? All ideas are appreciated,
data is a pandas dataframe. The desire output should look like this:
pop =
item_id rating_count
690 120
751 10
265 159
... ...
Note that itemsTrain contain unique item_ids in the rating dataset data.

you can do it this way:
In [200]: df = pd.DataFrame(np.random.randint(0,8,(15,2)),columns=['id', 'rating'])
In [201]: df
Out[201]:
id rating
0 4 6
1 0 1
2 2 4
3 2 5
4 2 7
5 3 5
6 6 1
7 4 3
8 4 3
9 3 2
10 2 4
11 7 7
12 3 1
13 2 7
14 7 3
In [202]: df.groupby('id').rating.count()
Out[202]:
id
0 1
2 5
3 3
4 3
6 1
7 2
Name: rating, dtype: int64
if you want to have result as a DF (you can also name the count column as you wish):
In [206]: df.groupby('id').rating.count().to_frame('count').reset_index()
Out[206]:
id count
0 0 1
1 2 5
2 3 3
3 4 3
4 6 1
5 7 2
you can also count # of unique ratings:
In [203]: df.groupby('id').rating.nunique()
Out[203]:
id
0 1
2 3
3 3
4 2
6 1
7 2
Name: rating, dtype: int64

You can use the method df.groupby() to group items by item_id and then use the method count() to sum the ratings.
Do as follows :
# df is your dataframe
v # the method allows you to sum values of the previous feature
df.groupby('item_id').rating.count()
^ ^ # the feature you want to sum upon its values
^
# The method allows you to group the samples by the feature "item_id"
# which is supposed to be unique

How to remove ugly row in pandas.dataframe

so I am filling dataframes from 2 different files. While those 2 files should have the same structure (the values should be different thought) the resulting dataframes look different. So when printing those I get:
a b c d
0 70402.14 70370.602112 0.533332 98
1 31362.21 31085.682726 1.912552 301
... ... ... ... ...
753919 64527.16 64510.008206 0.255541 71
753920 58077.61 58030.943621 0.835758 152
a b c d
index
0 118535.32 118480.657338 0.280282 47
1 49536.10 49372.999416 0.429902 86
... ... ... ... ...
753970 52112.95 52104.717927 0.356051 116
753971 37044.40 36915.264944 0.597472 165
So in the second dataframe there is that "index" row that doesnt make any sense for me and it causes troubles in my following code. I did neither write the code to fill the files into the dataframes nor I did create those files. So I am rather interested in checking if such a row exists and how I might be able to remove it. Does anyone have an idea about this?

The second dataframe has an index level named "index".
You can remove the name with
df.index.name = None
For example,
In [126]: df = pd.DataFrame(np.arange(15).reshape(5,3))
In [128]: df.index.name = 'index'
In [129]: df
Out[129]:
0 1 2
index
0 0 1 2
1 3 4 5
2 6 7 8
3 9 10 11
4 12 13 14
In [130]: df.index.name = None
In [131]: df
Out[131]:
0 1 2
0 0 1 2
1 3 4 5
2 6 7 8
3 9 10 11
4 12 13 14
The dataframe might have picked up the name "index" if you used reset_index and set_index like this:
In [138]: df.reset_index()
Out[138]:
index 0 1 2
0 0 0 1 2
1 1 3 4 5
2 2 6 7 8
3 3 9 10 11
4 4 12 13 14
In [140]: df.reset_index().set_index('index')
Out[140]:
0 1 2
index
0 0 1 2
1 3 4 5
2 6 7 8
3 9 10 11
4 12 13 14

Index is just the first column - it's numbering the rows by default, but you can change it a number of ways (e.g. filling it with values from one of the columns)