<jupyter_start><jupyter_text>Checking to see if there are any NaN values. [source](https://stackoverflow.com/questions/29530232/how-to-check-if-any-value-is-nan-in-a-pandas-dataframe)<jupyter_code>ntli.isnull().sum().sum()
hit_ntli = ntli[ntli['Hit'] == 1]
miss_ntli = ntli[ntli['Hit'] == 0]
hit_ntli.head()
miss_ntli.head()
del hit_ntli['Hit']
del miss_ntli['Hit']
miss_ntli.reset_index(drop=True, inplace=True)
miss_ntli.head()
hit_ntli.set_index('Compound', inplace=True)
miss_ntli.set_index('Compound', inplace=True)
zero_responses = []
for i in range(hit_ntli.shape[0]):
    zero_responses.append(hit_ntli.iloc[i].value_counts().loc[0.0])
hit_ntli['Zero_Responses'] = zero_responses
hit_ntli.head()
zero_responses = []
for i in range(miss_ntli.shape[0]):
    zero_responses.append(miss_ntli.iloc[i].value_counts().loc[0.0])
miss_ntli['Zero_Responses'] = zero_responses
miss_ntli.head()
fig1 = plt.figure(figsize=(20, 10))
hit_ax1 = fig1.add_subplot(121)
hit_ax1.hist(hit_ntli['Zero_Responses'], bins=[i*20 for i in range(9)])
hit_ax1.set_title('Hits')
hit_ax1.set_ylabel('Frequency')
hit_ax1.set_xlabel('Number of 0 Response Kinases (out of 190)')
miss_ax1 = fig1.add_subplot(122, sharey=hit_ax1)
miss_ax1.hist(miss_ntli['Zero_Responses'], bins=[i*20 for i in range(9)])
miss_ax1.set_title('Non-Hits')
miss_ax1.set_ylabel('Frequency')
miss_ax1.set_xlabel('Number of 0 Response Kinases (out of 190)')
plt.show()
pd.concat([hit_ntli['Zero_Responses'], miss_ntli['Zero_Responses']], axis=1).describe()
hit_ntli['Zero_Responses_Percent'] = hit_ntli['Zero_Responses'] / 190
miss_ntli['Zero_Responses_Percent'] = miss_ntli['Zero_Responses'] / 190
fig2 = plt.figure(figsize=(20, 10))
hit_ax2 = fig2.add_subplot(121)
hit_ax2.hist(hit_ntli['Zero_Responses_Percent'], bins=[i*0.1 for i in range(11)])
hit_ax2.set_title('Hits')
hit_ax2.set_ylabel('Frequency')
hit_ax2.set_xlabel('Percent of 0 Response Kinases (out of 190)')
miss_ax2 = fig2.add_subplot(122, sharey=hit_ax2)
miss_ax2.hist(miss_ntli['Zero_Responses_Percent'], bins=[i*0.1 for i in range(11)])
miss_ax2.set_title('Non-Hits')
miss_ax2.set_ylabel('Frequency')
miss_ax2.set_xlabel('Percent of 0 Response Kinases (out of 190)')
plt.show()<jupyter_output><empty_output>
{'fraction_non_alphanumeric': 0.16471647164716471, 'fraction_numerical': 0.045454545454545456, 'mean_word_length': 8.301255230125523, 'pattern_counts': {'":': 0, '<': 5, '<?xml version=': 0, '>': 5, 'https://': 1, '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': '1', 'dir': 'jupyter-structured-clean-dedup', 'id': '98195', 'license': 'MIT', 'n_tokens_mistral': 995, 'n_tokens_neox': 931, 'n_words': 138, 'path': 'pso/working_data/NTLI_EDA.ipynb', 'repo_name': 'nathancfox/cfs-reu-2018', 'size': '2222'}