I have multiple dicts (or sequences of key-value pairs) like this:
d1 = {key1: x1, key2: y1}
d2 = {key1: x2, key2: y2}
How can I efficiently get a result like this, as a new dict?
d = {key1: (x1, x2), key2: (y1, y2)}
….
I have two pandas dataframes:
from pandas import DataFrame
df1 = DataFrame({‘col1′:[1,2],’col2’:[3,4]})
df2 = DataFrame({‘col3’:[5,6]})
What is the best practice to get their cartesian product (of course without writing it explicitly like me)?
#df1, df2 cartesian product
df_cartesian = DataFrame({‘col1′:[1,2,1,2],’col2′:[3,4,3,4],’col3’:[5,5….
I have different dataframes and need to merge them together based on the date column. If I only had two dataframes, I could use df1.merge(df2, on=’date’), to do it with three dataframes, I use df1.merge(df2.merge(df3, on=’date’), on=’date’), however it becomes really complex and unreadable to do it with multiple dataframes.
All dataframes have one….
Is it possible to only merge some columns? I have a DataFrame df1 with columns x, y, z, and df2 with columns x, a ,b, c, d, e, f, etc.
I want to merge the two DataFrames on x, but I only want to merge columns df2.a, df2.b – not the entire DataFrame.
The result would be a DataFrame with x, y, z, a, b.
I could merge then delete the unwanted columns….
I need to merge multiple dictionaries, here’s what I have for instance:
dict1 = {1:{“a”:{A}}, 2:{“b”:{B}}}
dict2 = {2:{“c”:{C}}, 3:{“d”:{D}}
With A B C and D being leaves of the tree, like {“info1″:”value”, “info2″:”value2”}
There is an unknown level(depth) of dictionaries, it could be {2:{“c”:{“z”:{“y”:{C}}}}}
In my case it represents a directo….
I have the following dataframes:
> df1
id begin conditional confidence discoveryTechnique
0 278 56 false 0.0 1
1 421 18 false 0.0 1
> df2
concept
0 A
1 B
How do I merge on the indices to get:
id begin conditional confidence discoveryTechnique concept
0 278….
I have 3 CSV files. Each has the first column as the (string) names of people, while all the other columns in each dataframe are attributes of that person.
How can I “join” together all three CSV documents to create a single CSV with each row having all the attributes for each unique value of the person’s string name?
The join() function in panda….
How to do this in pandas:
I have a function extract_text_features on a single text column, returning multiple output columns. Specifically, the function returns 6 values.
The function works, however there doesn’t seem to be any proper return type (pandas DataFrame/ numpy array/ Python list) such that the output can get correctly assigned df.ix[: ,….
What is the Pythonic approach to achieve the following?
# Original lists:
list_a = [1, 2, 3, 4]
list_b = [5, 6, 7, 8]
# List of tuples from ‘list_a’ and ‘list_b’:
list_c = [(1,5), (2,6), (3,7), (4,8)]
Each member of list_c is a tuple, whose first member is from list_a and the second is from list_b.
….
How can I perform a (INNER| (LEFT|RIGHT|FULL) OUTER) JOIN with pandas?
How do I add NaNs for missing rows after a merge?
How do I get rid of NaNs after merging?
Can I merge on the index?
How do I merge multiple DataFrames?
Cross join with pandas
merge? join? concat? update? Who? What? Why?!
… and more. I’ve seen these recurring questions askin….