I have the following Pandas Dataframe
Which can also be generated using this list of dictionaries:
list_of_dictionaries = [
{'Project': 'A', 'Hours': 2, 'people_ids': [16986725, 17612732]},
{'Project': 'B', 'Hours': 2, 'people_ids': [17254707, 17567393, 17571668, 17613773]},
{'Project': 'C', 'Hours': 3, 'people_ids': [17097009, 17530240, 17530242, 17543865, 17584457, 17595079]},
{'Project': 'D', 'Hours': 2, 'people_ids': [17097009, 17584457, 17702185]}]
I have implemented kind of what I need, but adding columns vertically:
df['people_id1']=[x[0] for x in df['people_ids'].tolist()]
df['people_id2']=[x[1] for x in df['people_ids'].tolist()]
And then I get a different column of every single people_id, just until the second element, because when I add the extraction 3rd element on a third column, it crashes because , there is no 3rd element to extract from the first row.
Even though, what I am trying to do is to extract every people_id from people_ids column, and then each one of those will have their associated value from the Project and Hours columns, so I get a dataset like this one:
Any idea on how could I get this output?
I think what you are looking for is explode on 'people_ids' column.
df = df.explode('people_ids', ignore_index=True)
Related
Suppose I have a dataframe df, and it has columns with names 'a', 'b', 'c', 'd', 'e'. Then I make all combinations of length three (order doesn't matter) from this list to generate the following list of lists:
Combinations_of_3 = [ [['a','b','c'], ['a','b','d'],...., ['c','d','e']]]
Now I wish to create a for loop to populate a second data frame, and to do this I want to loop over Combinations_of_3 and use the current entry to select the corresponding columns of df.
For example, if I wanted to select only the 'a', 'b' and 'e' columns of df, I would normally write df[['a','b','e']]; but now I would like to do this in a for loop using Combinations_of_3. I'm writing this code using pandas / python. Thank you.
Just do as you described, using a variable:
Combinations_of_3 = [['a','b','c'], ['a','b','d'], ['c','d','e']]
for cols in Combinations_of_3:
#do something
print(df[cols])
NB. To create Combinations_of_3 you could use:
from itertools import combinations
Combinations_of_3 = list(combinations(df.columns, r=3))
#or using a generator
Combinations_of_3 = combinations(df.columns, r=3)
I have a Pandas DataFrame of ML experiment results (from MLFlow). I am trying to access the run_id of a single element in the 0th row and under the "tags" -> "run_id" multi-index in the columns.
The DataFrame is called experiment_results_df. I can access the element with the following command:
experiment_results_df.loc[0,(slice(None),'run_id')].values[0]
I thought I should be able to grab the value itself with a statement like the following:
experiment_results_df.at[0,('tags','run_id')]
# or...
experiment_results_df.loc[0,('tags','run_id')]
But either of those just results in the following rather confusing error (as I'm not setting anything):
ValueError: setting an array element with a sequence. The requested array has an inhomogeneous shape after 1 dimensions. The detected shape was (2,) + inhomogeneous part.
It's working now, but I'd prefer to use a simpler syntax. And more than that, I want to understand why the other approach isn't working, and if I can modify it. I find multiindexes very frustrating to work with in Pandas compared to regular indexes, but the additional formatting is nice when I print the DF to the console, or display it in a CSV viewer as I currently have 41 columns (and growing).
I don't understand what is the problem:
df = pd.DataFrame({('T', 'A'): {0: 1, 1: 4},
('T', 'B'): {0: 2, 1: 5},
('T', 'C'): {0: 3, 1: 6}})
print(df)
# Output
T
A B C
0 1 2 3
1 4 5 6
How to extract 1:
>>> df.loc[0, ('T', 'A')]
1
>>> df.at[0, ('T', 'A')]
1
>>> df.loc[0, (slice(None), 'A')][0]
1
I am following this program: https://scikit-learn.org/dev/auto_examples/inspection/plot_permutation_importance_multicollinear.html
since I have a problem with highly correlated features in my model (different from that one shown in the example). In this step
selected_features = [v[0] for v in cluster_id_to_feature_ids.values()]
I can get information on the features that I will need to remove from my classifier. They are given as numbers ([0, 3, 5, 6, 8, 9, 10, 17]). How can I get names of these features?
Ok, there are two different elements to this problem I think.
First, you need to get a list of the column names. In the example code you linked, it looks like the list of feature names is stored like this:
data.feature_names
Once you have the feature names, you'd need a way to loop through them and grab only the ones you want. Something like this should work:
columns = ['a', 'b', 'c', 'd']
keep_index = [0, 3]
new_columns = [columns[i] for i in keep_index]
new_columns
['a', 'b']
I have a dataframe with a very large number of columns of different types. I want to encode the categorical variables in my dataframe using get_dummies(). The question is: is there a way to get the column headers of the encoded categorical columns created by get_dummies()?
The hard way to do this would be to extract a list of all categorical variables in the dataframe, then append the different text labels associated to each categorical variable to the corresponding column headers. I wonder if there is an easier way to achieve the same end.
I think the way that should work with all the different uses of get_dummies would be:
#example data
import pandas as pd
df = pd.DataFrame({'P': ['p', 'q', 'p'], 'Q': ['q', 'p', 'r'],
'R': [2, 3, 4]})
dummies = pd.get_dummies(df)
#get column names that were not in the original dataframe
new_cols = dummies.columns[~dummies.columns.isin(df.columns)]
new_cols gives:
Index(['P_p', 'P_q', 'Q_p', 'Q_q', 'Q_r'], dtype='object')
I think the first column is the only column preserved when using get_dummies, so you could also just take the column names after the first column:
dummies.columns[1:]
which on this test data gives the same result:
Index(['P_p', 'P_q', 'Q_p', 'Q_q', 'Q_r'], dtype='object')
This question already has answers here:
Splitting dataframe into multiple dataframes
(13 answers)
Closed 3 years ago.
I have a large dataframe with hierarchical indexing (a simplistic/ format example provided in the code below). I would like to setup a loop/automated way of splitting the dataframe into subsets per unique index value, i.e. dfa, dfb, dfc etc. in the coded example below and store in a list.
I have tried the following but unfortunately to no success. Any help appreciated!
data = pd.Series(np.random.randn(9), index=[['a', 'a', 'a', 'b',
'b', 'c', 'c', 'd', 'd'], [1, 2, 3, 1, 2, 1, 2, 2, 3]])
split = []
for value in data.index.unique():
split.append(data[data.index == value])
I am not exactly sure if this is what you are looking for but have you checked groupby pandas function? The crucial part is that you can apply it across MultiIndex specifying which level of indexing (or what subset of levels) to group by. e.g.
split = {}
for value, split_group in data.groupby(level=0):
split[value] = split_group
print(split)
as #jezrael points out a simpler way to do it is:
dict(tuple(df.groupby(level=0)))