I have a pandas data frame like this:
df = pd.DataFrame({"Id": [1, 1, 1, 2, 2, 2, 2],
"Letter": ['A', 'B', 'C', 'A', 'D', 'B', 'C']})
How can I add a new column efficiently, "Merge" such that it concatenates all the values from the column "letter" by "Id", so the final data frame would look like this:
You can groupby Id column then transform
df['Merge'] = df.groupby('Id').transform(lambda x: '-'.join(x))
print(df)
Id Letter Merge
0 1 A A-B-C
1 1 B A-B-C
2 1 C A-B-C
3 2 A A-D-B-C
4 2 D A-D-B-C
5 2 B A-D-B-C
6 2 C A-D-B-C
Thanks for sammywemmy pointing out lambda is needless here, so you can use a simpler form
df['Merge'] = df.groupby('Id').transform('-'.join)
Related
I am working on aggregating the contents of a dataframe based on the range of values in a given column. My df looks like given below:
min max names
1 5 ['a','b']
0 5 ['d']
6 8 ['a','c']
3 4 ['e','a']
The output expected is
for min=0 and max=5, get the aggregated value, so the names value will be ['a','b','d','e','a']
for min=5 and max=10, get the aggregated value, the names value will be ['a','d']
Any help is appreciated.
The most intuitive approach would be to filter and then aggregate. To solve your specific problem, I would do this:
>> df = pd.DataFrame({"min": [1, 0, 6, 3],
"max": [5, 5, 8, 4],
"value": [['a','b'], ['d'], ['a','c'], ['e','a']]})
>> print(df)
min max value
0 1 5 [a, b]
1 0 5 [d]
2 6 8 [a, c]
3 3 4 [e, a]
>> sum_filtered_values = df[(df["max"]<=5) & (df["min"]>=0)].value.sum()
>> print(sum_filtered_values)
['a', 'b', 'd', 'e', 'a']
>> sum_filtered_values = df[(df["max"]<=10) & (df["min"]>=5)].value.sum()
>> print(sum_filtered_values)
['a', 'c']
I have a very large dataframe with over 2000 columns. I am trying to count the number of unique values for each column and filter out the columns with unique values below a certain number. Here is an example:
import pandas as pd
df = pd.DataFrame({'A': ('a', 'b', 'c', 'd', 'e', 'a', 'a'), 'B': (1, 1, 2, 1, 3, 3, 1)})
df.nunique()
A 5
B 3
dtype: int64
So lets say I wanna filter out column B which has lower than 5 unique values and return a df without column B.
Thanks-
Pass the .loc
df=df.loc[:,df.nunique()>3]
A
0 a
1 b
2 c
3 d
4 e
5 a
6 a
Others may have a more pythonic way. Try this out to see if it works.
x = df.nunique()
df[list(x[x>=5].index)]
What is the quickest way to merge to python data frames in this manner?
I have two data frames with similar structures (both have a primary key id and some value columns).
What I want to do is merge the two data frames based on id. Are there any ways do this based on pandas operations? How I've implemented it right now is as coded below:
import pandas as pd
a = pd.DataFrame({'id': [1,2,3], 'letter': ['a', 'b', 'c']})
b = pd.DataFrame({'id': [1,3,4], 'letter': ['A', 'C', 'D']})
a_dict = {e[id]: e for e in a.to_dict('record')}
b_dict = {e[id]: e for e in b.to_dict('record')}
c_dict = a_dict.copy()
c_dict.update(b_dict)
c = pd.DataFrame(list(c.values())
Here, c would be equivalent to
pd.DataFrame({'id': [1,2,3,4], 'letter':['A','b', 'C', 'D']})
id letter
0 1 A
1 2 b
2 3 C
3 4 D
combine_first
If 'id' is your primary key, then use it as your index.
b.set_index('id').combine_first(a.set_index('id')).reset_index()
id letter
0 1 A
1 2 b
2 3 C
3 4 D
merge with groupby
a.merge(b, 'outer', 'id').groupby(lambda x: x.split('_')[0], axis=1).last()
id letter
0 1 A
1 2 b
2 3 C
3 4 D
One way may be as following:
append dataframe a to dataframe b
drop duplicates based on id
sort values on remaining by id
reset index and drop older index
You can try:
import pandas as pd
a = pd.DataFrame({'id': [1,2,3], 'letter': ['a', 'b', 'c']})
b = pd.DataFrame({'id': [1,3,4], 'letter': ['A', 'C', 'D']})
c = b.append(a).drop_duplicates(subset='id').sort_values('id').reset_index(drop=True)
print(c)
Try this
c = pd.concat([a, b], axis=0).sort_values('letter').drop_duplicates('id', keep='first').sort_values('id')
c.reset_index(drop=True, inplace=True)
print(c)
id letter
0 1 A
1 2 b
2 3 C
3 4 D
I have given dataframe
Id Direction Load Unit
1 CN05059815 LoadFWD 0,0 NaN
2 CN05059815 LoadBWD 0,0 NaN
4 ....
....
and the given list.
list =['CN05059830','CN05059946','CN05060010','CN05060064' ...]
I would like to sort or group the data by a given element of the list.
For example,
The new data will have exactly the same sort as the list. The first column would start withCN05059815 which doesn't belong to the list, then the second CN05059830 CN05059946 ... are both belong to the list. With remaining the other data
One way is to use Categorical Data. Here's a minimal example:
# sample dataframe
df = pd.DataFrame({'col': ['A', 'B', 'C', 'D', 'E', 'F']})
# required ordering
lst = ['D', 'E', 'A', 'B']
# convert to categorical
df['col'] = df['col'].astype('category')
# set order, adding values not in lst to the front
order = list(set(df['col']) - set(lst)) + lst
# attach ordering information to categorical series
df['col'] = df['col'].cat.reorder_categories(order)
# apply ordering
df = df.sort_values('col')
print(df)
col
2 C
5 F
3 D
4 E
0 A
1 B
Consider below approach and example:
df = pd.DataFrame({
'col': ['a', 'b', 'c', 'd', 'e']
})
list_ = ['d', 'b', 'a']
print(df)
Output:
col
0 a
1 b
2 c
3 d
4 e
Then in order to sort the df with the list and its ordering:
df.reindex(df.assign(dummy=df['col'])['dummy'].apply(lambda x: list_.index(x) if x in list_ else -1).sort_values().index)
Output:
col
2 c
4 e
3 d
1 b
0 a
I usually use value_counts() to get the number of occurrences of a value. However, I deal now with large database-tables (cannot load it fully into RAM) and query the data in fractions of 1 month.
Is there a way to store the result of value_counts() and merge it with / add it to the next results?
I want to count the number user actions. Assume the following structure of
user-activity logs:
# month 1
id userId actionType
1 1 a
2 1 c
3 2 a
4 3 a
5 3 b
# month 2
id userId actionType
6 1 b
7 1 b
8 2 a
9 3 c
Using value_counts() on those produces:
# month 1
userId
1 2
2 1
3 2
# month 2
userId
1 2
2 1
3 1
Expected output:
# month 1+2
userId
1 4
2 2
3 3
Up until now, I just have found a method using groupby and sum:
# count users actions and remember them in new column
df1['count'] = df1.groupby(['userId'], sort=False)['id'].transform('count')
# delete not necessary columns
df1 = df1[['userId', 'count']]
# delete not necessary rows
df1 = df1.drop_duplicates(subset=['userId'])
# repeat
df2['count'] = df2.groupby(['userId'], sort=False)['id'].transform('count')
df2 = df2[['userId', 'count']]
df2 = df2.drop_duplicates(subset=['userId'])
# merge and sum up
print pd.concat([df1,df2]).groupby(['userId'], sort=False).sum()
What is the pythonic / pandas' way of merging the information of several series' (and dataframes) efficiently?
Let me suggest "add" and specify a fill value of 0. This has an advantage over the previously suggested answer in that it will work when the two Dataframes have non-identical sets of unique keys.
# Create frames
df1 = pd.DataFrame(
{'User_id': ['a', 'a', 'b', 'c', 'c', 'd'], 'a': [1, 1, 2, 3, 3, 5]})
df2 = pd.DataFrame(
{'User_id': ['a', 'a', 'b', 'b', 'c', 'c', 'c'], 'a': [1, 1, 2, 2, 3, 3, 4]})
Now add the the two sets of values_counts(). The fill_value argument will handle any NaN values that would arise, in this example, the 'd' that appears in df1, but not df2.
a = df1.User_id.value_counts()
b = df2.User_id.value_counts()
a.add(b,fill_value=0)
You can sum the series generated by the value_counts method directly:
#create frames
df= pd.DataFrame({'User_id': ['a','a','b','c','c'],'a':[1,1,2,3,3]})
df1= pd.DataFrame({'User_id': ['a','a','b','b','c','c','c'],'a':[1,1,2,2,3,3,4]})
sum the series:
df.User_id.value_counts() + df1.User_id.value_counts()
output:
a 4
b 3
c 5
dtype: int64
This is know as "Split-Apply-Combine". It is done in 1 line and 3-4 clicks, using a lambda function as follows.
1️⃣ paste this into your code:
df['total_for_this_label'] = df.groupby('label', as_index=False)['label'].transform(lambda x: x.count())
2️⃣ replace 3x label with the name of the column whose values you are counting (case-sensitive)
3️⃣ print df.head() to check it's worked correctly