If I have some DataFrame:
A B
1 a
1 a
1 b
1 c
1 c
1 d
2 a
2 b
2 b
2 d
I can use groupby() command and count the number of events from every unique line. But now, I don't want count all unique lines. I want just count how many unique values in column B correspond to some value in the column A. My output now should be
A UniqueValuesFromB
1 4
2 3
For this i did next code:
userNumber = []
items = []
for pos in df[column].unique():
dfEvent = df[df[column] == pos]
userNumber.append(len(numpy.unique(dfEvent[correspondingColumn])))
items.append(pos)
And then I do new new DF from new lists... What is a better way to code this fragment. Specially, I feel I can use here the groupby() command or similar one. Please, only pythonic way (or implementation of some pandas function) specially with the strong time performance improvement is asked.
Another way using value_counts, the key here is to pass the level number to count:
In [32]:
df.groupby('A')['B'].apply(pd.value_counts).count(level=0)
Out[32]:
A
1 4
2 3
dtype: int64
Another variant:
In [48]:
df.groupby('A')['B'].agg(lambda x: pd.Series.count(x.unique()))
Out[48]:
A
1 4
2 3
Name: B, dtype: int64
Use groupby and .nunique():
>>> df.groupby("A")["B"].nunique()
A
1 4
2 3
Name: B, dtype: int64
Related
I'm new in python.
I have data frame (DF) example:
id
type
1
A
1
B
2
C
2
B
I would like to add a column example A_flag group by id.
In the end I have data frame (DF):
id
type
A_flag
1
A
1
1
B
1
2
C
0
2
B
0
I can do this in two step:
DF['A_flag_tmp'] = [1 if x.type=='A' else 0 for x in DF.itertuples()]
DF['A_flag'] = DF.groupby(['id'])['A_flag_tmp'].transform(np.max)
It's working, but it's very slowy for big data frame.
Is there any way to optimize this case ?
Thank's for help.
Change your codes with slow iterative coding to fast vectorized coding by replacing your first step to generate a boolean series by Pandas built-in functions, e.g.
df['type'].eq('A')
Then, you can attach it to the groupby statement for second step, as follows:
df['A_flag'] = df['type'].eq('A').groupby(df['id']).transform('max').astype(int)
Result
print(df)
id type A_flag
0 1 A 1
1 1 B 1
2 2 C 0
3 2 B 0
In general, if you have more complicated conditions, you can also define it in vectorized way, eg. define the boolean series m by:
m = df['type'].eq('A') & df['type1'].gt(1) | (df['type2'] != 0)
Then, use it in step 2 as follows:
m.groupby(df['id']).transform('max').astype(int)
I'd need a little suggestion on a procedure using pandas, I have a 2-columns dataset that looks like this:
A 0.4533
B 0.2323
A 1.2343
A 1.2353
B 4.3521
C 3.2113
C 2.1233
.. ...
where first column contains strings and the second one floats. I would like to save the minimum value for each group of unique strings in order to have the associated minimum with A, B, C. Does anybody have any suggestions on that? It could help me also storing somehow all the values for each string they are associated.
Many thanks,
James
Input data:
>>> df
0 1
0 A 0.4533
1 B 0.2323
2 A 1.2343
3 A 1.2353
4 B 4.3521
5 C 3.2113
6 C 2.1233
Use groupby before min:
out = df.groupby(0).min()
Output result:
>>> out
1
0
A 0.4533
B 0.2323
C 2.1233
Update:
filter out all the values in the original dataset that are more than 20% different from the minimum
out = df[df.groupby(0)[1].apply(lambda x: x <= x.min() * 1.2)]
>>> out
0 1
0 A 0.4533
1 B 0.2323
6 C 2.1233
You can simply do it by
min_A=min(df[df["column_1"]=="A"]["value"])
min_B=min(df[df["column_1"]=="B"]["value"])
min_C=min(df[df["column_1"]=="C"]["value"])
where df = Dataframe column_1 and value are the names of the columns of the dataframe
You can also do it by using the pre-defined function of pandas i.e. groupby()
>> df.groupby(["column_1"]).min()
The Above will also give the same results.
I searched and I couldn't find a problem like mine. So if there is and somehow I couldn't find please let me know. So I can delete this post.
I stuck with a problem to split pandas dataframe into different data frames (df) by a value.
I have a dataset inside a text file and I store them as pandas dataframe that has only one column. There are more than one sets of information inside the dataset and a certain value defines the end of that set, you can see a sample below:
The Sample Input
In [8]: df
Out[8]:
var1
0 a
1 b
2 c
3 d
4 endValue
5 h
6 f
7 b
8 w
9 endValue
So I want to split this df into different data frames. I couldn't find a way to do that but I'm sure there must be an easy way. The format I display in sample output can be a wrong format. So, If you have a better idea I'd love to see. Thank you for help.
The sample output I'd like
var1
{[0 a
1 b
2 c
3 d
4 endValue]},
{[0 h
1 f
2 b
3 w
4 endValue]}
You could check where var1 is endValue, take the cumsum, and use the result as a custom grouper. Then Groupby and build a dictionary from the result:
d = dict(tuple(df.groupby(df.var1.eq('endValue').cumsum().shift(fill_value=0.))))
Or for a list of dataframes (effectively indexed in the same way):
l = [v for _,v in df.groupby(df.var1.eq('endValue').cumsum().shift(fill_value=0.))]
print(l[0])
var1
0 a
1 b
2 c
3 d
4 endValue
One idea with unique index values is replace non matched values to NaNs and backfilling them, last loop groupby object for list of DataFrames:
g = df.index.to_series().where(df['var1'].eq('endValue')).bfill()
dfs = [a for i, a in df.groupby(g, sort=False)]
print (dfs)
[ var1
0 a
1 b
2 c
3 d
4 endValue, var1
5 h
6 f
7 b
8 w
9 endValue]
I want to add an aggregate, grouped, nunique column to my pandas dataframe but not aggregate the entire dataframe. I'm trying to do this in one line and avoid creating a new aggregated object and merging that, etc.
my df has track, type, and id. I want the number of unique ids for each track/type combination as a new column in the table (but not collapse track/type combos in the resulting df). Same number of rows, 1 more column.
something like this isn't working:
df['n_unique_id'] = df.groupby(['track', 'type'])['id'].nunique()
nor is
df['n_unique_id'] = df.groupby(['track', 'type'])['id'].transform(nunique)
this last one works with some aggregating functions but not others. the following works (but is meaningless on my dataset):
df['n_unique_id'] = df.groupby(['track', 'type'])['id'].transform(sum)
in R this is easily done in data.table with
df[, n_unique_id := uniqueN(id), by = c('track', 'type')]
thanks!
df.groupby(['track', 'type'])['id'].transform(nunique)
Implies that there is a name nunique in the name space that performs some function. transform will take a function or a string that it knows a function for. nunique is definitely one of those strings.
As pointed out by #root, often the method that pandas will utilize to perform a transformation indicated by these strings are optimized and should generally be preferred to passing your own functions. This is True even for passing numpy functions in some cases.
For example transform('sum') should be preferred over transform(sum).
Try this instead
df.groupby(['track', 'type'])['id'].transform('nunique')
demo
df = pd.DataFrame(dict(
track=list('11112222'), type=list('AAAABBBB'), id=list('XXYZWWWW')))
print(df)
id track type
0 X 1 A
1 X 1 A
2 Y 1 A
3 Z 1 A
4 W 2 B
5 W 2 B
6 W 2 B
7 W 2 B
df.groupby(['track', 'type'])['id'].transform('nunique')
0 3
1 3
2 3
3 3
4 1
5 1
6 1
7 1
Name: id, dtype: int64
Very simple question everyone, but nearly impossible to find answers to basic questions in official documentation.
I have a dataframe object in Pandas that has rows and columns.
One of the columns, named "CBSM", contains boolean values. I need to delete all rows from the dataframe where the value of the CBSM column = "Y".
I see that there is a method called dataframe.drop()
Label, Axis, and Level are 3 parameters that the drop() method takes in. I have no clue what values to provide these parameters to accomplish my need of deleting the rows in the fashion I described above. I have a feeling the drop() method is not the right way to do what I want.
Please advise, thanks.
This method is called boolean indexing.
You can try loc with str.contains:
df.loc[~df['CBSM'].str.contains('Y')]
Sample:
print df
A CBSM L
0 1 Y 4
1 1 N 6
2 2 N 3
print df['CBSM'].str.contains('Y')
0 True
1 False
2 False
Name: CBSM, dtype: bool
#inverted boolean serie
print ~df['CBSM'].str.contains('Y')
0 False
1 True
2 True
Name: CBSM, dtype: bool
print df.loc[~df['CBSM'].str.contains('Y')]
A CBSM L
1 1 N 6
2 2 N 3
Or:
print df.loc[~(df['CBSM'] == 'Y')]
A CBSM L
1 1 N 6
2 2 N 3