I have two pandas dataframes with some columns in common. These columns are of type category but unfortunately the category codes don't match for the two dataframes. For example I have:
>>> df1
artist song
0 The Killers Mr Brightside
1 David Guetta Memories
2 Estelle Come Over
3 The Killers Human
>>> df2
artist date
0 The Killers 2010
1 David Guetta 2012
2 Estelle 2005
3 The Killers 2006
But:
>>> df1['artist'].cat.codes
0 55
1 78
2 93
3 55
Whereas:
>>> df2['artist'].cat.codes
0 99
1 12
2 23
3 99
What I would like is for my second dataframe df2 to take the same category codes as the first one df1 without changing the category values. Is there any way to do this?
(Edit)
Here is a screenshot of my two dataframes. Essentially I want the song_tags to have the same cat codes for artist_name and track_name as the songs dataframe. Also song_tags is created from a merge between songs and another tag dataframe (which contains song data and their tags, without the user information) and then saved and loaded through pickle. Also it might be relevant to add that I had to cast artist_name and track_name in song_tags to type category from type object.
I think essentially my question is: how to modify category codes of an existing dataframe column?
Let's say I have a categorical variable with the following values, given by calling unique() on the column in the dataframe:
Categories (7, object): [0-2, 6-8, 9-11, 3-5, 15-17, 12-14, 24-26]
and that I have the following occurrences for each of these categories given by calling value_counts():
0-2 209
3-5 34
6-8 17
9-11 7
15-17 6
12-14 3
24-26 1
what would be a good way to coarsen/compress these categories into two new categories "high" and "low"?
This is using pd.cut with the right value of the range, and cut them into two , also you can using qcut to get different cut result
groupkey=pd.cut(s.index.str.split('-').str[-1].astype(int),2,labels=['low','high'])
s.groupby(groupkey).sum()
low 270
high 7
Name: v, dtype: int64
I currently have a massive dataset with a large amount of rows and I wanted to create a smaller dataframe that only pulls 2 columns from the larger one and how many times each name occurred in that chapter in this instance 'Occurrence'
The below code is what I am using
df1 = (Dec16.groupby(["BNF Chapter", "Name"]).size().reset_index(name="Occurrence"))
df1
It plots this
BNF Chapter Name Occurrence
1 Aluminium hydroxide 2
1 Aluminium hydroxide + Magnesium trisilicate 2
1 Alverine 702
.......
21 Polihexanide 2
21 Potassium hydroxide 32
21 Sesame oil 22
21 Sodium chloride 222
What I would like to get is the top 10 most occurred names for a certain chapter as the dataset is so large.
For example a dataframe that only pulls
The top 10 most common names in chapter 1
How would I go about doing this?
Many thanks!!!
You can use this pandas.DataFrame.count
This Count Values In Pandas Dataframe here can help you out I hope
I have the following pandas dataframe with 50,000 unique rows and 20 columns (included is a snippet of the relevant columns):
df1:
PRODUCT_ID PRODUCT_DESCRIPTION
0 165985858958 "Fish Burger with Lettuce"
1 185965653252 "Chicken Salad with Dressing"
2 165958565556 "Pork and Honey Rissoles"
3 655262522233 "Cheese, Ham and Tomato Sandwich"
4 857485966653 "Coleslaw with Yoghurt Dressing"
5 524156285551 "Lemon and Raspberry Cheesecake"
I also have the following dataframe (which I also have saved in dictionary form) which has 2 columns and 20,000 unique rows:
df2 (also saved as dict_2)
PROD_ID PROD_DESCRIPTION
0 548576 "Fish Burger"
1 156956 "Chckn Salad w/Ranch Dressing"
2 257848 "Rissoles - Lamb & Rosemary"
3 298770 "Lemn C-cake"
4 651452 "Potato Salad with Bacon"
5 100256 "Cheese Cake - Lemon Raspberry Coulis"
What I am wanting to do is compare the "PRODUCT_DESCRIPTION" field in df1 to the the "PROD_DESCRIPTION" field in df2 and find the closest match/matches to help with the heavy lifting part. I would then need to manually check the matches but it would be a lot quicker The ideal outcome would look like this, e.g. with one or more part matches noted:
PRODUCT_ID PRODUCT_DESCRIPTION PROD_ID PROD_DESCRIPTION
0 165985858958 "Fish Burger with Lettuce" 548576 "Fish Burger"
1 185965653252 "Chicken Salad with Dressing" 156956 "Chckn Salad w/Ranch Dressing"
2 165958565556 "Pork and Honey Rissoles" 257848 "Rissoles - Lamb & Rosemary"
3 655262522233 "Cheese, Ham and Tomato Sandwich" NaN NaN
4 857485966653 "Coleslaw with Yoghurt Dressing" NaN NaN
5 524156285551 "Lemon and Raspberry Cheesecake" 298770 "Lemn C-cake"
6 524156285551 "Lemon and Raspberry Cheesecake" 100256 "Cheese Cake - Lemon Raspberry Coulis"
I have already completed a join which has identified the exact matches. It's not important that the index is retained as the Product ID's in each df are unique. The results can also be saved into a new dataframe as this will then be applied to a third dataframe that has around 14 million rows.
I've used the following questions and answers (amongst others):
Is it possible to do fuzzy match merge with python pandas
Fuzzy merge match with duplicates including trying jellyfish module as suggested in one of the answers
Python fuzzy matching fuzzywuzzy keep only the best match
Fuzzy match items in a column of an array
and also various loops/functions/mapping etc. but have had no success, either getting the first "fuzzy match" which has a low score or no matches being detected.
I like the idea of a matching/distance score column being generated as per here as it would then allow me to speed up the manual checking process.
I'm using Python 2.7, pandas and have fuzzywuzzy installed.
using fuzz.ratio as my distance metric, calculate my distance matrix like this
df3 = pd.DataFrame(index=df.index, columns=df2.index)
for i in df3.index:
for j in df3.columns:
vi = df.get_value(i, 'PRODUCT_DESCRIPTION')
vj = df2.get_value(j, 'PROD_DESCRIPTION')
df3.set_value(
i, j, fuzz.ratio(vi, vj))
print(df3)
0 1 2 3 4 5
0 63 15 24 23 34 27
1 26 84 19 21 52 32
2 18 31 33 12 35 34
3 10 31 35 10 41 42
4 29 52 32 10 42 12
5 15 28 21 49 8 55
Set a threshold for acceptable distance. I set 50
Find the index value (for df2) that has maximum value for every row.
threshold = df3.max(1) > 50
idxmax = df3.idxmax(1)
Make assignments
df['PROD_ID'] = np.where(threshold, df2.loc[idxmax, 'PROD_ID'].values, np.nan)
df['PROD_DESCRIPTION'] = np.where(threshold, df2.loc[idxmax, 'PROD_DESCRIPTION'].values, np.nan)
df
You should be able to iterate over both dataframes and populate either a dict of a 3rd dataframe with your desired information:
d = {
'df1_id': [],
'df1_prod_desc': [],
'df2_id': [],
'df2_prod_desc': [],
'fuzzywuzzy_sim': []
}
for _, df1_row in df1.iterrows():
for _, df2_row in df2.iterrows():
d['df1_id'] = df1_row['PRODUCT_ID']
...
df3 = pd.DataFrame.from_dict(d)
I don't have enough reputation to be able to comment on answer from #piRSquared. Hence this answer.
The definition of 'vi' and 'vj' didn't go through with an error (AttributeError: 'DataFrame' object has no attribute 'get_value'). It worked when I inserted an "underscore". E.g. vi = df._get_value(i, 'PRODUCT_DESCRIPTION')
Similar issue persisted for 'set_value' and the same solution worked there too. E.g. df3._set_value(i, j, fuzz.ratio(vi, vj))
Generating idxmax posed another error (TypeError: reduction operation 'argmax' not allowed for this dtype) which was because contents of df3 (the fuzzy ratios) were of type 'object'. I converted all of them to numeric just before defining threshold and it worked. E.g. df3 = df3.apply(pd.to_numeric)
A million thanks to #piRSquared for the solution. For a Python novice like me, it worked like a charm. I am posting this answer to make it easy for other newbies like me.
I have a pandas data-frame that looks like this:
ID Hobbby Name
1 Travel Kevin
2 Photo Andrew
3 Travel Kevin
4 Cars NaN
5 Photo Andrew
6 Football NaN
.............. 1303 rows.
The number of Names filled in might be large then 2 as well. I would like to end up the entire Names column filled n equally into the names ( or+1 in the case of even number of rows). I already store into a variable number of names the total number of names. In the above case it's 2. I tried filtering and counting by each name but I don't know how to make this when the number of name is dynamic.
Expected Dataframe:
ID Hobbby Name
1 Travel Kevin
2 Photo Andrew
3 Travel Kevin
4 Cars Kevin
5 Photo Andrew
6 Football Andrew
I tried: replace NaN with 0 in Column Name using fillna. Filter the column and end up with a dataframe that has only the na fields and afterwards len(df) to get the number of nan and from here created 2 databases each containing half of the df. Bu I think this approach is completely wrong as I do not always have 2 Names. There could be2,3,4 etc. ( this is given by a dictionary)
Any help highly appreciated
Thanks.
It's difficult to tell but I think you need ffill
df['Name'] = df['Name'].ffill()