Writing multiple arrays in dictionary to column

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I have a dictionary which looks like:

dict = {'a':[1,2,3],

I want to write each array into a column in a file, such that the output looks like:

1 4 7
2 5 6
3 6 9

where values are tab separated. So far I have tried:

import csv
with open('text.csv', 'w') as f:
    writer = csv.writer(f, delimiter='\t')
    for k,array in dict.items():

But this just prints numbers 1 to 9 in a single column. Can someone help me with this?

(Is there any more efficient way to store multiple arrays like shown in the dictionary such that it becomes easier to write on a file?)

You can use zip after unpacking the dict values:

with open('text.csv', 'w') as f:
    writer = csv.writer(f, delimiter='\t')

Associating Multiple Values with Each Key in a Dictionary, You need a dictionary that maps each key to multiple values. Solution. By nature, a dictionary is a one-to-one mapping, but it's not hard� I have data in a dictionary object and need to load it into a two column listbox. The VBA help says you can load data into a multicolumn listbox from a 2D array. So my question is how can I extract the data from a dictionary object directly into a 2D array. The dictionary object stores data in key and item pairs.

You can modify from here and then write the output to the file. I'm sure there are cleaner ways to do it, but this does what you're asking. Though this assumes the rows are all the same length. If all columns don't have data, this will not produce the desired output.

# data in
dict = {'a':[1,2,3],

cols = []

for k, v in dict.items():
    cur_col = 0
    for i in v:
            cur_col += 1
        except IndexError as e:
            cur_col +=1
    cur_col = 0

for row in cols:


1   4   7
2   5   8
3   6   9

How to Create Pandas Dataframe from Multiple Lists? Pandas , is of type int. We want to make a dataframe with these lists as columns. Create pandas dataframe from lists using dictionary. One approach� As serialized data structures, Python programmers intensively use arrays, lists, and dictionaries. Storing these data structures persistently requires either a file or a database to work with. This article describes how to write a list to file, and how to read that list back into memory. To write data in a file [/writing-files-using-python/], and to read data from a file [/reading-files-with

If you are open to using pandas, it's fairly straightforward. I would also suggest not using dict as a variable name since it's a builtin function.

import pandas as pd

arr_dict = {'a': [1, 2, 3], 'b': [4, 5, 6], 'c': [7, 8, 9]}
pd.DataFrame(arr_dict).to_csv('test.csv', index=False, sep='\t')

If arrays are of different length, You'll have to be a bit more creative. Many examples are in this post with one of them here:

pd.DataFrame.from_dict(arr_dict, orient='index').T

Python, Given a list of nested dictionary, write a Python program to create a Pandas dataframe using it. Step #3: Pivoting dataframe and assigning column names. Iterate over columns in dataframe using Column Names. Dataframe.columns returns a sequence of column names. We can iterate over these column names and for each column name we can select the column contents by column name i.e.

Creating Pandas dataframe using list of lists, It contains well written, well thought and well explained computer science and Pandas DataFrame is a 2-dimensional labeled data structure with columns of potentially different types. Pandas DataFrame can be created in multiple ways. dictionary, and numpy array in Pandas � Get a list of a specified column of a� Here are the steps to create an array of arrays: Create an array and store it in a variable. Create additional arrays, and store them in variables as well. Use the previously created arrays and separate them with the comma operator. Assign the arrays to a variable. The following code creates four arrays and stores the arrays in variables.

pandas.DataFrame.from_dict — pandas 0.25.3 documentation, Creates DataFrame object from dictionary by columns or by index allowing dtype specification. Parameters: data : dict. Of the form {field : array-like}� Dictionary comprehension is a method for transforming one dictionary into another dictionary. During this transformation, items within the original dictionary can be conditionally included in the new dictionary and each item can be transformed as needed. A good list comprehension can make your code more expressive and thus, easier to read.

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  • stackoverflow.com/questions/6740918/… This might help you with writing a dictionary from a csv.