A Python package to generate csv/json from command line. It allows you to create CSV/JSON files by asking you to manually enter data for each cells row by row in Terminal (Windows CMD / Bash).
Project description
kaggle_dataset_creator - A Python package to generate csv/json
A Python package that allows you to create CSV/JSON files by manually entering each of the entries of cells row by row in Terminal (Windows CMD / Bash).
Installation
Open terminal and enter the below command (Python 3).
pip install kaggle_dataset_creator
Features
-
It allows you to create your own CSV file if you are looking for creating a CSV with manually entered data. You can also get the JSON version of the entered data.
-
You can also view your data at any point of time in your Terminal and again continue to enter data if you wish to add more rows/records for your final CSV/JSON file.
Note: Currently the package is in development, it will be released soon.
Example
from kaggle_dataset_creator import KaggleDataSet
kd = KaggleDataSet()
kd.start()
print(kd.columns)
print(kd.container)
kd.view(); # To view the final DataFrame on Terminal
kd.to_csv(); # To save in csv, default file name is take if filename is not provided
print("DATA:- ")
print(kd.dataset) # Accessing dataset attribute to get the final DataFrame
print('Total rows: ', kd.rows)
print('Types: ', kd.data_types)
If you want to try above in the terminal, try as below after installation.
In next version, it will be released with more features. Here our intension is to get the final CSV/JSON.
>>> from kaggle_dataset_creator import KaggleDataSet
>>>
>>> kd = KaggleDataSet()
>>> kd.start()
Enter number of columns that you want in your dataset: 3
SUCCESS: You are successfully done with no. of columns
Enter the name of 1st column: fullname
Enter the name of 2nd column: age
Enter the name of 3rd column: salary
SUCCESS: You are successfully done with the column names
[DATA ENTRY] <row: 1> fullname : Raj Shekhar
[DATA ENTRY] <row: 1> age : 45
[DATA ENTRY] <row: 1> salary : 600000
==================================================
Do you want to add 1 more row / view data (y/n/v): y
==================================================
[DATA ENTRY] <row: 2> fullname : Venc Bell
[DATA ENTRY] <row: 2> age : 67
[DATA ENTRY] <row: 2> salary : 900000
==================================================
Do you want to add 1 more row / view data (y/n/v): y
==================================================
[DATA ENTRY] <row: 3> fullname : Robert Grime
[DATA ENTRY] <row: 3> age : 89
[DATA ENTRY] <row: 3> salary : 9000000
==================================================
Do you want to add 1 more row / view data (y/n/v): v
--------------------------------------------------
fullname age salary
0 Raj Shekhar 45 600000
1 Venc Bell 67 900000
2 Robert Grime 89 9000000
--------------------------------------------------
==================================================
Do you want to add 1 more row / view data (y/n/v): y
==================================================
[DATA ENTRY] <row: 4> fullname : Elen Goom
[DATA ENTRY] <row: 4> age : 55
[DATA ENTRY] <row: 4> salary : 800000
==================================================
Do you want to add 1 more row / view data (y/n/v): y
==================================================
[DATA ENTRY] <row: 5> fullname : Rita Ora
[DATA ENTRY] <row: 5> age : 36
[DATA ENTRY] <row: 5> salary : 9900000
==================================================
Do you want to add 1 more row / view data (y/n/v): v
--------------------------------------------------
fullname age salary
0 Raj Shekhar 45 600000
1 Venc Bell 67 900000
2 Robert Grime 89 9000000
3 Elen Goom 55 800000
4 Rita Ora 36 9900000
--------------------------------------------------
==================================================
Do you want to add 1 more row / view data (y/n/v): y
==================================================
[DATA ENTRY] <row: 6> fullname : Senso Tomy
[DATA ENTRY] <row: 6> age : 54
[DATA ENTRY] <row: 6> salary : 7700000
==================================================
Do you want to add 1 more row / view data (y/n/v): n
Is this mistakenly typed (y/n): n
==================================================
SUCCESS: You are successfully done with entering data for your dataset
>>>
>>> # View the data
...
>>> kd.view()
--------------------------------------------------
fullname age salary
0 Raj Shekhar 45 600000
1 Venc Bell 67 900000
2 Robert Grime 89 9000000
3 Elen Goom 55 800000
4 Rita Ora 36 9900000
5 Senso Tomy 54 7700000
--------------------------------------------------
True
>>>
>>> success = kd.view()
--------------------------------------------------
fullname age salary
0 Raj Shekhar 45 600000
1 Venc Bell 67 900000
2 Robert Grime 89 9000000
3 Elen Goom 55 800000
4 Rita Ora 36 9900000
5 Senso Tomy 54 7700000
--------------------------------------------------
>>>
>>> success
True
>>>
>>> # Store the dataset as DataFrame
...
>>> df = kd.dataset
>>> df
fullname age salary
0 Raj Shekhar 45 600000
1 Venc Bell 67 900000
2 Robert Grime 89 9000000
3 Elen Goom 55 800000
4 Rita Ora 36 9900000
5 Senso Tomy 54 7700000
>>>
>>> type(df)
<class 'pandas.core.frame.DataFrame'>
>>>
>>> kd.rows
6
>>>
>>> kd.data_types
{'fullname': 'string', 'age': 'numeric', 'salary': 'numeric'}
>>>
Generating random strings
Python 3.6.7 (v3.6.7:6ec5cf24b7, Oct 20 2018, 03:02:14)
[GCC 4.2.1 Compatible Apple LLVM 6.0 (clang-600.0.57)] on darwin
Type "help", "copyright", "credits" or "license" for more information.
>>>
>>> from kaggle_dataset_creator.random_string import random_string
>>>
>>> random_string()
'VFdwQmVFOV'
>>>
>>> random_string()
'TWpBeE9TMH'
>>>
>>> random_string()
'=UDN0gDN54'
>>>
>>> random_string()
'TWpBeE9TMH'
>>>
>>> random_string()
'=ATM1UDMz4'
>>>
>>> random_string(11)
'VFdwQmVFOVR'
>>>
>>> random_string(15)
'5M2RW5kTUVVRxAT'
>>>
>>> random_string(15)
'VFdwQmVFOVRNSGR'
>>>
>>> random_string(15)
'5M2RS9kQUFVR5sW'
>>>
>>> random_string(15)
'=AzN2MDMy4iNzoz'
>>>
>>> random_string(15)
'MjAxOS0wNS0yMSA'
>>>
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