Overall, the primary data type being processed in the code is a string, but various other data types (like dictionaries and floats) are used to store intermediate results and perform calculations.

from collections import Counter
import math
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns

# Provided data
data = '3=U³\\¬¶6|cò\\u000fã£Ü\\u001bn>]UãÊOM³YWl®cÕ\\u0017«ÔñqZ­ÓZÖø\\u005cæ\\u0017ÙGµZ.ôSv²­5\\u001f;Ì͸Õ\'Ö<\\u001eYã.ËôðâøxãµtøªÓ3/VÍƵrÜfÚczlzjÎvfñfÎÔO\\u00177iËG§tÍ£=ðÙ\\u0017챺+¼=êqÇV\\u005cG«ig\']+>geµÜñ\\u001e¶±§ÊÚx|<͸|¥ìáÚ.é\\u001bn£³¦]véeô<y¸ãÉã\\u001dò>Ö\\u001e¼Æv\'§êÌvtn6Ó¥³læ:µl\'>jélOfÇ7ÉkÌWÔ\\u001fSÕå\'§\\u001e\\u001fÉ®\\u001b§\\u001bnáx;Åô¥¶gu¦­ÊÍcÓÖÑ©¹ð¶KêÊ>\\u001b;9«ª|K¹\\u001eÜ£;.¶ÅWðø´Ü£Õæxs\\u005c®\\u005cìÌuÑÓimn²\\u001f6Ö\\u005c]VÓ¬êÆôðkcm\\u005cÚ¦|iv\\u001døUOK³.>xm6vf¹en²vMñ.OSkS:sM¶´\\u001f<;ð;\\u001e[q;67Myj]VÚcz²µM§Å³±¬O+òtm3­¦©ÓGn9y<ÇZ;\\u001eÅÚ>ÑÓز¹\\u001eÚY/Gãð³\\u001by£zÒÎNµxø\\u005c­Uám\\u001eÕVκ67.z¼rÜc¹l³ÒñãNγ.Çfº9ñâ®l±¶<¶GÙ\\u0017§isêÚ¦øt«¥/él7:Õ¸ñ5>lñ[3æØ|SnGѵ:>â;Ôj>-<WGN|¥W5uSã©mZømÇ3­S[¥v+m²¼VUìrÕxãYÙMWìc>3ÖØø¬Õ+Ó\\u001bmZÙÃ\\u001dØÍc«9ñæVËÌW<ÕY³:êqéiGÓ\\u005cÜéÖZgSÙNéÌnÌ=qø®ÃÓ6^<\\u0017ÍK[¥å\\u001dæÔWSs:®jvÊ^j«:ÍGñSåÑ[\\u005cÕ^\\u001b^¦Ú\\u000fÇrÇSÚ´yqì\\u001dã´yɵ+>^j]Ysé¼ä;£­ZÇzrãV/ÅÓNvM«Ëi].§±;:ñ6ͬô-ºÅò±WÌ^Åy:Nvè­\\u000f¼cÖ5^ª\\u001f-ÖY=KñGÓ-Õ´ØUnѶªòÔôr¼<«.W5åm¥|Ñãª>fòØ7âñM§9^\\u000f^Åã±|eêÑÓr;¬ôV[SÇtÇ5znµ:7Mnq\\u001f6|ÆÍæK¹xã¸]+³NÇ£áñcÙÆìÊ[yK¼Nãx;¶[ÙÌkâ³\\u001eÅÜ´]-[Îr­Sò\\u001f\'>Ã|:mÆ|²ÉØ«£Ü£¶´Ír§3Ç<¶xñÊ­¦/âê<ôVµÒ/Mu+òاªyj¹KÕfná|\\u001e­t\\u001flkÅkzNôÚtÌÔêjøÃËVu´uÌÙ|¼èêèÜ´m馫£ºq츹+ÖèÜG\\u000fÜèË\\u001b\\u001bºxvÑg´OxËÒ\\u001f<[MÚô¥zÑ/âÖÑ­MæU­Y|5µ6¶xÓ©\\u001e³â®ä|Zg/á§rW©§\\u005cÙØ|ªn-Õª>MÇÑ/ªµtÎr¶Ø\\u001fâò[Ô\\u001f­iÇä³­´­µÖÌn¬mø3s3|jå¼É§\\u001bu¥ø©Oz<7|ÃÓf®\\u001bø\\u001bê3g.Ó±.¼eueô©ñg\\u001dܱÚjWÆ7ry-ê²/Ìê+ÜÔ\\u001fìf[ðÍSåؼܱåeéWjOÃOÒÊ7è]Æ6­Õغ6s;ÃñG˱éMãKºZæÚ\\u001e¹GêU\\u001f|èrv¸vqÖVô9nnÆè\\u001fÅ\\u001fKºµ¬º\\u001eµð/KW9ÙjÎU6ìÉ\\u001f\\u001eÕG;èÜi¼\\u001e^ávù£=¥3Ü3ktytºKÎòtÓ\\u000fº:^-µÑåfµYváòONO-ÙUµÆË3µ±¶©n<§ò'

def analyze_data(data):
    frequency = Counter(data)
    total_chars = sum(frequency.values())
    expected_frequency = total_chars / len(frequency)
    entropy = -sum((freq / total_chars) * math.log2(freq / total_chars) for freq in frequency.values())
    print('Entropy:', entropy)
    print('Character Frequency:')
    for char, freq in frequency.items():
        print(f'{char}: {freq}')

    # Known file headers (magic numbers)
    file_signatures = {
        b'\x89PNG': 'PNG Image',
        b'GIF8': 'GIF Image',
        b'\xFF\xD8': 'JPEG Image',
        b'%PDF': 'PDF Document',
        b'PK': 'ZIP Archive',
        b'RIFF': 'WAV/AVI File',
        b'\x7FELF': 'ELF Executable',
        b'\x42\x5A': 'BZ2 Compressed',
    }

    # Check for file signatures
    for signature, file_type in file_signatures.items():
        if data.encode('utf-8').startswith(signature):
            print(f'Identified file format: {file_type}')
            return

    print('File format could not be identified.')

    # Frequency Test
    freq_deviation = {char: freq - expected_frequency for char, freq in frequency.items()}
    print('Frequency Test Deviation:')
    for char, deviation in freq_deviation.items():
        print(f'{char}: {deviation}')

    # Runs Test
    runs = 0
    last_char = None
    for char in data:
        if char != last_char:
            runs += 1
        last_char = char
    print(f'Runs Test: {runs} runs found.')

    # Chi-Squared Test
    chi_squared = sum((freq - expected_frequency) ** 2 / expected_frequency for freq in frequency.values())
    print(f'Chi-Squared Test Statistic: {chi_squared}')

    # Extract features
    features = {'entropy': entropy}
    features.update(frequency)
    df = pd.DataFrame(list(features.items()), columns=['Feature', 'Value'])
    print('Extracted Features:')
    print(df)

    # Visualize character frequencies
    plt.figure(figsize=(12, 6))
    sns.set_style('whitegrid')
    sns.barplot(x=list(frequency.keys()), y=list(frequency.values()))
    plt.title('Character Frequency Distribution')
    plt.xlabel('Characters')
    plt.ylabel('Frequency')
    plt.xticks(rotation=90)
    plt.tight_layout()
    plt.show()

    # Visualize entropy
    plt.figure(figsize=(8, 4))
    sns.set_style('whitegrid')
    plt.plot([entropy], marker='o')
    plt.title('Entropy Visualization')
    plt.xlabel('Segment')
    plt.ylabel('Entropy')
    plt.grid()
    plt.show()

# Call the analyze_data function
analyze_data(data)