String to JSON Compare using Pandas

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I am using Pandas' DataFrames. I have a case like this after merging 2 files: fr.item = "ipod"; fr.bucket = {'ipad':34,'ipod':36,'iwatch':27} Note: Type of the data is Series here

Is there a way I can check for item in bucket (ipod) and fetch value (36) here? Also, doing it without a loop is appreciated as I'm doing a column to column compare.

Inputs

item    bucket
ipod    {'ipad':34,'ipod':36,'iwatch':27}
ipad    {'ipad':87,'ipod':31,'iwatch':62}

Output

36
87

Agree with @bazingaa, just use:

fr.bucket[fr.item]

So get the dictionary key with naming ipod then get it's value like above, that's it.

pandas.DataFrame.to_json, A string representing the compression to use in the output file, only used when the first argument is a Whether to include the index values in the JSON string. In this post we will learn how to import a JSON File, JSON String, JSON API Response and import it to Pandas dataframe and work with it. Pandas has built-in function read_json to import the JSON Strings and Files into pandas dataframe and json_normalize function works with nested json but it’s little hard to understand how to use it.

Try this:

# update col1 and col2 to the name of your item and bucket column
col1 = 'item'
col2 = 'bucket'
output = fr.apply(lambda row: row[col2][col1], axis=1)

It'll return a pandas series of 36, 87.

Update: In case your items in bucket column have type str, try the following first:

import ast
fr.loc[:, col2] = fr[col2].apply(ast.literal_eval)

pandas.read_json, Indication of expected JSON string format. Compatible JSON strings can be produced by to_json() with a corresponding orient value. The set of possible orients  If so, you can use the following template to load your JSON string into the DataFrame: import pandas as pd pd.read_json (r'Path where you saved the JSON file\File Name.json') In this short guide, I’ll review the steps to load different JSON strings into Python using pandas. Steps to Load JSON String into Pandas DataFrame Step 1: Prepare the JSON String. To start with a simple example, let’s say that you have the following data about different products and their prices:

item=['ipod','ipad']
bucket=[{'ipad':34,'ipod':36,'iwatch':27},{'ipad':87,'ipod':31,'iwatch':62}]

result_output=[]    # Defining for output
counter=0           # For initialising the counter through the bucket's elements
for key in item:    # For each row value in column1. Column1 is item.
   temp_bucket=dict(bucket[counter]) # For each column's value,it is as a dictionary
   if key in temp_bucket:   # Checking if the column1's value is present in the dictionary of that row (column2)   
      length_1=len(bucket)-1                 # Need to subtract 1 as python is based on 0 indexing     
      result_output.append(temp_bucket[key]) # Appending each row output to the result list.
      if counter<length_1:                   # Need to check if there is sufficient element is present in column2. Otherwise throw error for index.
         counter=counter+1                  # for checking the next element of column2

print(result_output) # Output list

pandas.io.json.json_normalize, R interface · pandas Ecosystem · Comparison with R / R libraries · Comparison with SQL · Comparison with SAS · API Reference pandas.io.json. json_normalize (data, record_path=None, meta=None, meta : list of paths (​string or list of strings), default None. Fields to use as metadata for each record in resulting table. Often with Python and Pandas you import data from outside - CSV, JSON etc - and the data format could be different from the one you expect. For example dates and numbers can come as strings. This cause problems when you need to group and sort by this values stored as strings instead of a their correct type. Fortunately pandas offers quick and easy way of converting dataframe columns. In this

I tried this, it worked

for i in df.index:

#For Bucket
val_bucket=ast.literal_eval(bucket[i])
for key, val in val_bucket.items():
    if my_items[i]== key:
        print ("Value",val)
         break

So, I have converted those JSON values (which was in series) to dict and extract the value

Pandas, set limits on the length and character contents of strings. Working with large JSON datasets can be deteriorating, particularly when they are too large to fit into​  Convert the object to a JSON string. Note NaN’s and None will be converted to null and datetime objects will be converted to UNIX timestamps. Parameters path_or_buf str or file handle, optional. File path or object. If not specified, the result is returned as a string. orient str. Indication of expected JSON string format. Series:

How to Load JSON String into Pandas DataFrame, In this short guide, I'll review the steps to load different JSON strings into Python using pandas. Steps to Load JSON String into Pandas DataFrame. Step 1:  min_rowsint, optional. The number of rows to display in the console in a truncated repr (when number of rows is above max_rows ). max_colsint, optional. Maximum number of columns to display in the console. show_dimensionsbool, default False. Display DataFrame dimensions (number of rows by number of columns).

Handle JSON Data Using JSON and Pandas in Python, We use strings as the keys, and values can be strings and numbers, as well as other valid JSON data types. Each key-value pair is associated  Parsing of JSON Dataset using pandas is much more convenient. Pandas allow you to convert a list of lists into a Dataframe and specify the column names separately. A JSON parser transforms a JSON text into another representation must accept all texts that conform to the JSON grammar. It may accept non-JSON forms or extensions.

JSON Data in Python, Let's compare the data types in Python and JSON. Python, JSON. dict, Object. list, Array. tuple, Array. str, String. With the pandas library, this is as easy as using two commands! pandas.read_json() To convert a JSON string to a pandas object (either a series or dataframe). Then, assuming the results were stored as df: df.to_csv() Which can either return a string or write directly to a csv-file. This can be too time consuming though, did you try any online

Comments
  • How about fr.bucket[fr.item]?
  • item bucket ipod {'ipad':34,'ipod':36,'iwatch':27} ipad {'ipad':87,'ipod':31,'iwatch':22} result should be 36 87
  • @Bazingaa can you check the expected output and help please
  • AttributeError: ("'Series' object has no attribute 'bucket'", 'occurred at index 0')
  • TypeError: ('string indices must be integers', 'occurred at index 0')
  • @Lal So is your column names 'col1' and 'col2' instead of 'bucket' and 'item'?