I started writing Python scripts for my research this past summer, and have been picking up the language as I go. For my current work, I have a dictionary of lists, sample_range_dict, that is initialized with descriptor_cols as the keys and empty lists for values. Sample code is below:
import numpy as np
import pandas as pd
def rangeFunc(arr):
return (np.max(arr) - np.min(arr))
df_sample = pd.DataFrame(np.random.rand(2000, 4), columns=list("ABCD")) #random dataframe for testing
col_list = df_sample.columns
sample_range_dict = dict.fromkeys(col_list, []) #creates dictionary where each key pairs with an empty list
rand_df = df_sample.sample(n=20) #make a new dataframe with 20 random rows of df_sample
I want to go through each column from rand_df and calculate the range of values, putting each range in the list with the specified column name (e.g. sample_range_dict["A"] = [range in column A]). The following is the code I initially thought to use for this:
for d in col_list:
sample_range_dict[d].append(rangeFunc(rand_df[d].tolist()))
However, instead of each key having one item in the list, printing sample_range_dict shows each key having an identical list of 4 values:
{'A': [0.8404352070810013,
0.9766398946246098,
0.9364714925930782,
0.9801082480908744],
'B': [0.8404352070810013,
0.9766398946246098,
0.9364714925930782,
0.9801082480908744],
'C': [0.8404352070810013,
0.9766398946246098,
0.9364714925930782,
0.9801082480908744],
'D': [0.8404352070810013,
0.9766398946246098,
0.9364714925930782,
0.9801082480908744]}
I've determined that the first value is the range for "A", second value is the range for "B", and so on. My question is about why this is happening, and how I could rewrite the code in order to get one item in the list for each key.
P.S. I'm looking to make this an iterative process, hence using lists instead of single numbers.
The issue is this line:
sample_range_dict = dict.fromkeys(col_list, [])
You only created one list. You don't have four lists with the same elements; you have one list, and four references to it. When you add to it via one reference, the element is visible through the other references, because it's the same list:
>>> a = dict.fromkeys(['x', 'y', 'z'], [])
>>> a['x'] is a['y']
True
>>> a['x'].append(5)
>>> a['y']
[5]
If you want each key to have a different list, either create a new list for each key:
>>> a = { k: [] for k in ['x', 'y', 'z'] }
>>> a['x'] is a['y']
False
>>> a['x'].append(5)
>>> a['y']
[]
Or use a defaultdict which will do it for you:
>>> from collections import defaultdict
>>> a = defaultdict(list)
>>> a['x'] is a['y']
False
>>> a['x'].append(5)
>>> a['y']
[]
Related
I have only been able to create a two column data frame from a defaultdict (termed output):
df_mydata = pd.DataFrame([(k, v) for k, v in output.items()],
columns=['id', 'value'])
What I would like to be able to do is using this basic format also initiate the dataframe with three columns: 'id', 'id2' and 'value'. I have a separate defined dict that contains the necessary look up info, called id_lookup.
So I tried:
df_mydata = pd.DataFrame([(k, id_lookup[k], v) for k, v in output.items()],
columns=['id', 'id2','value'])
I think I'm doing it right, but I get key errors. I will only know if id_lookup is exhaustive for all possible encounters in hindsight. For my purposes, simply putting it all together and placing 'N/A` or something for those types of errors will be acceptable.
Would the above be appropriate for calculating a new column of data using a defaultdict and a simple lookup dict, and how might I make it robust to key errors?
Here is an example of how you could do this:
import pandas as pd
from collections import defaultdict
df = pd.DataFrame({'id': [1, 2, 3, 4],
'value': [10, 20, 30, 40]})
id_lookup = {1: 'A', 2: 'B', 3: 'C'}
new_column = defaultdict(str)
# Loop through the df and populate the defaultdict
for index, row in df.iterrows():
try:
new_column[index] = id_lookup[row['id']]
except KeyError:
new_column[index] = 'N/A'
# Convert the defaultdict to a Series and add it as a new column in the df
df['id2'] = pd.Series(new_column)
# Print the updated DataFrame
print(df)
which gives:
id value id2
0 1 10 A
1 2 20 B
2 3 30 C
3 4 40 N/A
I am doing a K-means project and I have to do it by hand, which is why I am trying to figure out what is the best ways to group things according to their last values into a list or a dictionary. Here is what I am talking about
list_of_tuples = [(honey,1),(bee,2),(tree,5),(flower,2),(computer,5),(key,1)]
Now my ultimate goal is to be able to sort out the list and have 3 different lists each with its respected element
"""This is the goal"""
list_1 = [honey,key]
list_2 = [bee,flower]
list_3 = [tree, computer]
I can use a lot of if statements and a for loop, but is there a more efficient way to do it?
If you're not opposed to using something like pandas, you could do something along these lines:
import pandas as pd
list_1, list_2, list_3 = pd.DataFrame(list_of_tuples).groupby(1)[0].apply(list).values
Result:
In [19]: list_1
Out[19]: ['honey', 'key']
In [20]: list_2
Out[20]: ['bee', 'flower']
In [21]: list_3
Out[21]: ['tree', 'computer']
Explanation:
pd.DataFrame(list_of_tuples).groupby(1) groups your list of tuples by the value at index 1, then you extract the values as lists of index 0 with [0].apply(list).values. This gives you an array of lists as below:
array([list(['honey', 'key']), list(['bee', 'flower']),
list(['tree', 'computer'])], dtype=object)
Something to the effect can be achieved with a dictionary and a for loop, using the second element of the tuple as a key value.
list_of_tuples = [("honey",1),("bee",2),("tree",5),("flower",2),("computer",5),("key",1)]
dict_list = {}
for t in list_of_tuples:
# create key and a single element list if key doesn't exist yet
# append to existing list otherwise
if t[1] not in dict_list.keys():
dict_list[t[1]] = [t[0]]
else:
dict_list[t[1]].append( t[0] )
list_1, list_2, list_3 = dict_list.values()
d = {'A': ['A11117',
'33465'
'17160144',
'A11-33465',
'3040',
'A11-33465 W1',
'nor'], 'B': ['maD', 'vern', 'first', 'A2lRights']}
I have a dictionary d and I would like to sort the values based on length of characters. For instance, for key A the value A11-33465 W1 would be first because it contains 12 characters followed by 'A11-33465' because it contains 9 characters etc. I would like this output:
d = {'A': ['A11-33465 W1',
' A11-33465',
'17160144',
'A11117',
'33465',
'3040',
'nor'],
'B': ['A2lRights',
'first',
'vern',
'maD']}
(I understand that dictionaries are not able to be sorted but I have examples below that didn't work for me but the answer contains a dictionary that was sorted)
I have tried the following
python sorting dictionary by length of values
print(' '.join(sorted(d, key=lambda k: len(d[k]), reverse=True)))
Sort a dictionary by length of the value
sorted_items = sorted(d.items(), key = lambda item : len(item[1]))
newd = dict(sorted_items[-2:])
How do I sort a dictionary by value?
import operator
sorted_x = sorted(d.items(), key=operator.itemgetter(1))
But they both do not give me what I am looking for.
How do I get my desired output?
You are not sorting the dict, you are sorting the lists inside it. The simplest will be a loop that sorts the lists in-place:
for k, lst in d.items():
lst.sort(key=len, reverse=True)
This will turn d into:
{'A': ['3346517160144', 'A11-33465 W1', 'A11-33465', 'A11117', '3040', 'nor'],
'B': ['A2lRights', 'first', 'vern', 'maD']}
If you want to keep the original data intact, use a comprehension like:
sorted_d = {k: sorted(lst, key=len, reverse=True) for k, lst in d.items()}
I have 6 data sets. Their names are: e10_all, e11_all, e12_all, e13_all, e14_all, and e19_all.
All have different numbers of columns and rows, but with some common columns. I need to append the rows of these columns together. First, I want to determine the columns that are common to all of the data sets, so I know which columns to select in my SQL query.
In R, I am able to do this using:
# Create list of dts
list_df = list(e10_all, e11_all, e12_all, e13_all, e14_all, e19_all)
col_common = colnames(list_df[[1]])
# Write for loop
for (i in 2:length(list_df)){
col_common = intersect(col_common, colnames(list_df[[i]]))
}
# View the common columns
col_common
# Get as a comma-separated list
cat(noquote(paste(col_common, collapse = ',')))
I want to do the same thing, but in Python. Does anyone happen to know a way?
Thank you
It's not that different in pandas. Making some dummy dataframes:
>>> import pandas as pd
>>> e10_all = pd.DataFrame({"A": [1,2], "B": [2,3], "C": [2,3]})
>>> e11_all = pd.DataFrame({"B": [4,5], "C": [5,6]})
>>> e12_all = pd.DataFrame({"B": [1,2], "C": [3,4], "M": [8,9]})
Then your code would translate to something like
>>> list_df = [e10_all, e11_all, e12_all]
>>> col_common = set.intersection(*(set(df.columns) for df in list_df))
>>> col_common
{'C', 'B'}
>>> ','.join(sorted(col_common))
'B,C'
That second line turns each of the frames' columns into a set and then takes the intersection of all of them. A more literal translation of your code would work too, although we tend to avoid writing loops where we can avoid it, and we tend to loop over elements directly (for df in list_df[1:]:) rather than going via index. Still,
col_common = set(list_df[0].columns)
for i in range(1, len(list_df)):
col_common = col_common.intersection(list_df[i].columns)
would get the job done.
I'm sure this has been asked and answered, but I cant find it. I have this dictionary:
{'22775': 15.9,
'22778': 29.2,
'22776': 20.25,
'22773': 9.65,
'22777': 22.9,
'22774': 12.45}
a string and a float.
I want to list the key strings in a tk listbox to allow the user to select one and then use the corresponding float in a calculation to determine a delay factor in an event.
I have this code:
def dic_entry(line):
#Create key:value pairs from string
key, sep, value = line.strip().partition(":")
return key, float(value)
with open(filename1) as f_obj:
s = dict(dic_entry(line) for line in f_obj)
print (s) #for testing only
s_ord = sorted(s.items(),key=lambda x: x[1])
print (s_ord)
The first print gets me
{'22775': 15.9,
'22778': 29.2,
'22776': 20.25,
'22773': 9.65,
'22777': 22.9,
'22774': 12.45}
as expected. The second, which I hoped would give me an ordered list of keys gets me
[('22773', 9.65),
('22774', 12.45),
('22775', 15.9),
('22776', 20.25),
('22777', 22.9),
('22778', 29.2)].
I have tried using sorteddictionary from the collections module and it gives me a sorted dictionary, but I'm having trouble extracting a list of keys.
s_ord2 = []
for keys in s.items():
s_ord2.append (keys)
print (s_ord2)
gives me a list of key value pairs:
[('22776', 20.25),
('22777', 22.9),
('22774', 12.45),
('22773', 9.65),
('22778', 29.2),
('22775', 15.9)]
I'm sure I'm doing something dumb, I just don't know what it is.
You're using items when you want to use keys:
In [1]: d = {'z': 3, 'b': 4, 'a': 9}
In [2]: sorted(d.keys())
Out[2]: ['a', 'b', 'z']
In [3]: sorted(d.items())
Out[3]: [('a', 9), ('b', 4), ('z', 3)]
d.items() gives you tuples of (key, value); d.keys() just gives you just the keys.