Remove Rows with NaN from pandas DataFrame in Python (Example) | How to Drop & Delete Missing Data

How to drop rows containing NaN values in a pandas DataFrame in the Python programming language. More details: https://statisticsglobe.com/remove-rows-with-nan-of-pandas-dataframe-in-python
Python code of this video:

import pandas as pd # Load pandas

data = pd.DataFrame( # Create DataFrame with NaN values
{"x1":[1, 2, float("NaN"), 4, 5, 6],
"x2":["a", "b", float("NaN"), float("NaN"), "e", "f"],
"x3":[float("NaN"), 10, float("NaN"), float("NaN"), 12, 13]})
print(data) # Print DataFrame with NaN values

data1 = data.dropna() # Apply dropna() function
print(data1) # Print updated DataFrame

data2a = data.dropna(subset = ["x2"]) # Apply dropna() function
print(data2a) # Print updated DataFrame

data2b = data[data["x2"].notna()] # Apply notna() function
print(data2b) # Print updated DataFrame

data2c = data[pd.notnull(data["x2"])] # Apply notnull() function
print(data2c) # Print updated DataFrame

data3a = data.dropna(how = "all") # Apply dropna() function
print(data3a) # Print updated DataFrame

data3b = data[data.notna().any(axis = 1)] # Apply notna() function
print(data3b) # Print updated DataFrame

data3c = data[data.notnull().any(axis = 1)] # Apply notnull() function
print(data3c) # Print updated DataFrame

data4 = data.dropna(thresh = 2) # Apply dropna() function
print(data4) # Print updated DataFrame

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