Download csv dictionary

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CSV to ParquetThis repo is archived and the code moved to Arrow CLI Tools.Convert CSV files to Apache Parquet. You may also be interested in json2parquet, csv2arrow, or json2arrow.InstallationDownload prebuilt binariesYou can get the latest releases from Cargocargo install csv2parquetUsage Arguments: Input CSV file Output fileOptions: -s, --schema-file File with Arrow schema in JSON format --max-read-records The number of records to infer the schema from. All rows if not present. Setting max-read-records to zero will stop schema inference and all columns will be string typed --header Set whether the CSV file has headers [possible values: true, false] -d, --delimiter Set the CSV file's column delimiter as a byte character [default: ,] -c, --compression Set the compression [possible values: uncompressed, snappy, gzip, lzo, brotli, lz4, zstd] -e, --encoding Sets encoding for any column [possible values: plain, rle, bit-packed, delta-binary-packed, delta-length-byte-array, delta-byte-array, rle-dictionary] --data-pagesize-limit Sets data page size limit --dictionary-pagesize-limit Sets dictionary page size limit --write-batch-size Sets write batch size --max-row-group-size Sets max size for a row group --created-by Sets "created by" property --dictionary Sets flag to enable/disable dictionary encoding for any column --statistics Sets flag to enable/disable statistics for any column [possible values: none, chunk, page] --max-statistics-size Sets max statistics size for any column. Applicable only if statistics are enabled -p, --print-schema Print the schema to stderr -n, --dry Only print the schema -h, --help Print help information -V, --version Print version information">Usage: csv2parquet [OPTIONS] Arguments: Input CSV file Output fileOptions: -s, --schema-file File with Arrow schema in JSON format --max-read-records The number of records to infer the schema from. All rows if not present. Setting max-read-records to zero will stop schema inference and all columns will be string typed --header Set whether the CSV file has headers [possible values: true, false] -d, --delimiter Set the CSV file's column delimiter as a byte character [default: ,] -c, --compression Set the compression [possible values: uncompressed, snappy, gzip, lzo, brotli, lz4, zstd] -e, --encoding Sets encoding for any column [possible values: plain, rle, bit-packed, delta-binary-packed, delta-length-byte-array, delta-byte-array, rle-dictionary] --data-pagesize-limit Sets data page size limit --dictionary-pagesize-limit Sets dictionary page size limit --write-batch-size Sets write batch size --max-row-group-size Sets max size for a row group --created-by Sets "created by" property --dictionary Sets flag to enable/disable dictionary encoding for any column --statistics Sets flag to enable/disable statistics for any column [possible values: none, chunk, page] --max-statistics-size Sets max statistics size for any column. Applicable only if statistics are enabled -p, --print-schema Print the schema to stderr -n, --dry Only print the schema -h, --help Print help information -V, --version Print version informationThe --schema-file option uses the same file format as --dry and --print-schema.ExamplesConvert a CSV to Parquetcsv2parquet data.csv data.parquetConvert a CSV with no header to Parquet

= open("Emp.pkl",'rb')ID = pickle.load(fout)print(ID[5])AnswerOutputDunzoExplanationimport pickle — Imports the pickle module, which is used for serializing and deserializing Python objects.ID = {1: "Ziva", 2: "53050", 3: "IT", 4: "38", 5: "Dunzo"} — Defines a dictionary named ID with keys and values.fin = open("Emp.pkl", "wb") — Opens a file named Emp.pkl in binary write mode ("wb").pickle.dump(ID, fin) — Serializes the ID dictionary and writes it to the file handle fin using the pickle.dump() function.fin.close() — Closes the file fin after writing the pickled data.fout = open("Emp.pkl", 'rb') — Opens the file Emp.pkl again, this time in binary read mode ("rb"), to read the pickled data.ID = pickle.load(fout) — Deserializes the data from the file fout using the pickle.load() function and assigns it to the variable ID. This effectively restores the original dictionary from the pickled data.print(ID[5]) — Prints the value associated with key 5 in the restored ID dictionary, which is "Dunzo".Question 12What will be the output of the following code ?import pickleList1 = ['Roza', {'a': 23, 'b': True}, (1, 2, 3), [['dogs', 'cats'], None]] List2 = ['Rita', {'x': 45, 'y': False}, (9, 5, 3), [['insects', 'bees'], None]] with open('data.pkl', 'wb') as f: f.write(List1)with open('data.pkl', 'wb') as f: f.write(List2) with open('data.pkl', 'rb') as f: List1 = pickle.load(f)print(List1)AnswerThe code raises an error because write() function does not work in binary file. To write an object on to a binary file dump() function of pickle module is used.Question 13What is the output of the following considering the file data.csv given below. File data.csv contains:Identifier;First name;Last name901242;Riya;Verma207074;Laura;Grey408129;Ali;Baig934600;Manit;Kaur507916;Jiva;Jainimport csvwith open('C:\data.csv', 'r+') as f: data = csv.reader(f) for row in data: if 'the' in row : print(row)AnswerThis code will produce no output.ExplanationBy default, csv.reader() uses a comma (,) as the delimiter to separate values in a CSV file. But the delimiter in the file data.csv is semicolon (;), hence the rows won't split correctly, leading to each row being treated as a single string. When the code checks if the row contains the word 'the', it will only print rows where 'the' appears in the entire row. Therefore, the given code will not output anything.Question 14(a)Identify the error in the following code.import csvf = open('attendees1.csv')csv_f = csv.writer(f)Answerimport csvf = open('attendees1.csv') #errorcsv_f = csv.writer(f)To use the csv.writer() function, the file should be opened in write mode ('w'). The corrected code is :import csvf = open('attendees1.csv', 'w')csv_f = csv.writer(f)Question 14(b)Identify the error in the following code.import csvf = open('attendees1.csv')csv_f = csv.reader()for row in csv_f: print(row)Answerimport csvf = open('attendees1.csv') #error 1csv_f = csv.reader() #error 2for row in csv_f: print(row)To use the csv.reader() function, the file should be opened in read mode ('r').The reader object should be in syntax = csv.reader().The corrected code is :import csvf = open('attendees1.csv', 'r')csv_f = csv.reader(f)for