<jupyter_start><jupyter_text>Fifa potential and overall classification    - 85+: 85above    - 75 to 84: 75to84    - 65 to 74: 65to74    - below 65: below65<jupyter_code>df['potential_fifa'] = 0

for i in tqdm(range(len(df))):
    if(df.pot_fifa[i] >= 85):
        df['potential_fifa'][i] = '85above'
    elif(85 > df.pot_fifa[i] > 74):
        df['potential_fifa'][i] = '75to84'
    elif(75>df.pot_fifa[i]>64):
        df['potential_fifa'][i] = '65to74'
    else:
        df['potential_fifa'][i] = 'below65'
              
df.potential_fifa.value_counts()
df['overall_fifa'] = 0

for i in tqdm(range(len(df))):
    if(df.ovr_fifa[i] >= 85):
        df['overall_fifa'][i] = '85above'
    elif(85 > df.ovr_fifa[i] > 74):
        df['overall_fifa'][i] = '75to84'
    elif(75>df.ovr_fifa[i]>64):
        df['overall_fifa'][i] = '65to74'
    else:
        df['overall_fifa'][i] = 'below65'
              
df.overall_fifa.value_counts()
# Converting height in meters to feet
df['height'] = df['height']*3.28<jupyter_output><empty_output><jupyter_text>Height classification - 6.5 feet above : 6.5above - 6 to 6.5: 6to6.5 - 5.5 to 6: 5.5to6 - below 5.5: below5.5<jupyter_code>df['new_height'] = 0

for i in tqdm(range(len(df))):
    if(df.height[i] >= 6.5):
        df['new_height'][i] = '6.5above'
    elif(6.5 > df.height[i] >5.9):
        df['new_height'][i] = '6to6.5'
    elif(6>df.height[i]>5.5):
        df['new_height'][i] = '5.5to6'
    else:
        df['new_height'][i] = 'below5.5'
df['new_height'].value_counts()<jupyter_output><empty_output><jupyter_text>Classifying Appearances - 300 above: 300above - 150 to 300 : 150to300 - 50 to 150: 50to150 - below 50: below50<jupyter_code>df['appearances'] = 0

for i in tqdm(range(len(df))):
    if(df.overall_app[i] >= 300):
        df['appearances'][i] = '300above'
    elif(300 > df.overall_app[i] > 149):
        df['appearances'][i] = '150to300'
    elif(150 >df.overall_app[i]>49):
        df['appearances'][i] = '50to150'
    else:
        df['appearances'][i] = 'below50'
              
df.appearances.value_counts()<jupyter_output><empty_output><jupyter_text>Classifying goals- above 100 : above100- 50 to 100: 50to100- 20 to 50 :20to50- below 20 : below20<jupyter_code>df['goals'] = 0

for i in tqdm(range(len(df))):
    if(df.overall_gls[i] >= 100):
        df['goals'][i] = '100above'
    elif(100 > df.overall_gls[i] > 49):
        df['goals'][i] = '50to100'
    elif(50 >df.overall_gls[i]>19):
        df['goals'][i] = '20to50'
    else:
        df['goals'][i] = 'below20'
              
df.goals.value_counts()
df.to_csv('../transfers2.1.csv')<jupyter_output><empty_output>
{'fraction_non_alphanumeric': 0.18716780561883067, 'fraction_numerical': 0.08845861807137434, 'mean_word_length': 3.263754045307443, 'pattern_counts': {'":': 0, '<': 17, '<?xml version=': 0, '>': 42, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
{'chain_length': '4', 'dir': 'jupyter-structured-clean-dedup', 'id': '115157', 'license': 'MIT', 'n_tokens_mistral': 1282, 'n_tokens_neox': 1123, 'n_words': 181, 'path': 'projects/causal moneyball/Causal-analysis-on-football-transfer-prices/Pre-processing notebooks/categorizing_scraped_data.ipynb', 'repo_name': 'afurmanov/causalML', 'size': '2634'}