<jupyter_start><jupyter_text>All about handling missing data in various scenerios.   What all pandas consider as null values?> Is values like 'nan' in string format also null values?     >> 'na' > pd.isnull,pd.notnull, pd.notna How can we customize these settings and also manually add new types of null values?<jupyter_code># importing pandas as pd
import pandas as pd

# importing numpy as np
import numpy as np

# dictionary of lists
dict = {'First Score':[100, 90, np.nan, 95],
		'Second Score': [30, 45, 56, np.nan],
		'Third Score':[np.nan, 40, 80, np.nan]}

# creating a dataframe from list
df = pd.DataFrame(dict)
df
# It shows all columns with nan values. 
_null= df.isnull().sum()
# Per column based frequency of 'nan' values in the dataframe so that we can drop 
# the columns with significant number of 'nan' values.
_null.sort_values(ascending=False, inplace=True)
_null<jupyter_output><empty_output>
{'fraction_non_alphanumeric': 0.09857612267250822, 'fraction_numerical': 0.018619934282584884, 'mean_word_length': 4.6770186335403725, 'pattern_counts': {'":': 0, '<': 5, '<?xml version=': 0, '>': 9, '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': '1', 'dir': 'jupyter-structured-clean-dedup', 'id': '8713', 'license': 'MIT', 'n_tokens_mistral': 309, 'n_tokens_neox': 284, 'n_words': 122, 'path': 'docs/source/notebooks/FeatureEngineering/MissingData.ipynb', 'repo_name': 'sandy1618/LearnAI', 'size': '913'}