using multiple dictionaries to write a csv file in python

This is a way how you can do it. I am sure there are better ways too.

import pandas as pd
dict2 = {"apple": 1, "banana": 2}
dict1 = {1: [30], 2: [42]}

df = pd.DataFrame(list(dict1.items()), columns=['product code','average sales'])
df['average sales'] = df['average sales'].str[0] #removing the square brackets
df2 = pd.DataFrame(list(dict2.items()), columns=['product name','xx'])
df3 = pd.concat([df,df2],axis=1).iloc[:, 0:3] #taking only the first 3 columns
print(df3)
df3.to_csv('file.csv', index=False)

There are a few ways to do this correctly. I think the following is quite instructive, especially if you aren't too familiar with Pandas.

d1 = {1:[30], 2:[42]}
d2 = {"apple":1, "banana":2}


def get_key(d, val):
    '''return key for any value'''
    for k, v in d.items():
        if val == v:
             return k
    return "key doesn't exist"


#Make a new dict containing all the columns you need
r = {'product name':[], 'product code':[],  'average sales':[]}

#Populate r
for k,v in d1.items():
    r['product code'].append(k)
    r['average sales'].append(*v) #* unpacks the list item
    r['product name'].append(get_key(d2, k))
    
#Make a DataFrame and export it
import pandas as pd
df = pd.DataFrame(r)
df.to_csv('your_file.csv', index=False)

You can merge the dictionaries, then convert to pandas dataframe, and then write the dataframe to csv:

import pandas as pd
dict1 = {1:[30], 2:[42]}
dict2 = {"apple":1, "banana":2}

#merge dict1 and dict2 into dict3:
dict3 = {k: [v, dict1[v][0]] for k, v in dict2.items()}

#create pandas dataframe from dict3 and transpose it.
df = pd.DataFrame(dict3).transpose()

#wite dataframe to csv file
df.to_csv("new_file.csv", header=None)

Here is the resulting new_file.csv:

apple,1,30
banana,2,42

import csv

dict1 = {1:[30], 2:[42]}
dict2 = {"apple":1, "banana":2}

# Final array, will be written to csv
data = []

for name, code in dict2:
    if code in dict:
        avg_sales = dict1[code][0]
        data += [name, code, avg_sales]
    # else key doesn't exist

with open('myfile.csv', 'w', newline='') as file:
    mywriter = csv.writer(file, delimiter=',')
    mywriter.writerow(["Product Name", "Product Code", "Average Sales"])   # Headers
    mywriter.writerows(data)    # Data

Btw, it looks like your values in dict1 are single element arrays, so I'm using dict1[code][0]. If you change it to just integer values, it'll just be dict1[code].

You could iterate through the keys of dict2, to build each row by using

pd.DataFrame({'product name': k, 'product code': dict2[k], 'average sales': dict1[dict2[k]]})

and append it row by row to the df. The solution code is

import pandas as pd
dict1 = {1:[30], 2:[42]}
dict2 = {"apple":1, "banana":2}

df = pd.DataFrame()   #empty df
for k in dict2:
    df = df.append(pd.DataFrame({'product name': k, 
                                 'product code': dict2[k], 
                                 'average sales': dict1[dict2[k]]  }))
df.to_csv('my_file.csv', index=False)

Output