<jupyter_start><jupyter_text><jupyter_code>import pandas as pd
import numpy as np
ckd_data = pd.read_csv("https://raw.githubusercontent.com/dphi-official/Datasets/master/Chronic%20Kidney%20Disease%20(CKD)%20Dataset/ChronicKidneyDisease.csv")
ckd_data.head()
ckd_data.dtypes.value_counts()
ckd_data.classification.value_counts()
data2 = ckd_data.loc[247:253, ["id", "age", "classification"]]
data2
ckd_data.age.max()
ckd_data.age.min()
ckd_data.shape
ckd_data.notnull().sum()
ckd_data.describe()
ckd_data.dtypes
#Finding the mean of the column having NaN
median_value=ckd_data['bp'].median()
median_value
  
# # Replace NaNs in column S2 with the
# # mean of values in the same column
# gfg['G2'].fillna(value=mean_value, inplace=True)
# print('Updated Dataframe:')
# # print(gfg)

ckd_data['bp'].fillna(value=median_value, inplace=True)
ckd_data.describe()
ckd_data.dtypes
ckd_data.notnull().sum()<jupyter_output><empty_output>
{'fraction_non_alphanumeric': 0.14455231930960086, 'fraction_numerical': 0.019417475728155338, 'mean_word_length': 7.672897196261682, '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': '195913', 'license': 'Apache-2.0', 'n_tokens_mistral': 389, 'n_tokens_neox': 367, 'n_words': 59, 'path': 'Pandas_Quiz_Dphi.ipynb', 'repo_name': 'Advaiti/bert', 'size': '927'}