8. Handling Missing Data with Pandas in Python | Pandas Tutorial in Hindi | Pandas in Python

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Links to other videos:
1. Introduction to Python: https://youtu.be/_zmbJ-RGLR8
2. Loops and Control Structures: https://youtu.be/qf72IqqSivA
3. NumPy Arrays: https://youtu.be/qkqcBLX1E7w
4. NumPy Indexing and Selection: https://youtu.be/hSAriOpnfXI
5. NumPy Operations: https://youtu.be/I7xGXqoO6DA
6. Pandas in Python: https://youtu.be/G7zYxavyvvA
7. DataFrames in Pandas: https://youtu.be/fiw-X3oIbOY
8. Handling missing data with Pandas: https://youtu.be/y05nFM-y4gw
9. Pandas operations: https://youtu.be/CHCAGsiAy8k
10. Exploratory Data Analysis: https://youtu.be/sEAGLOa5At8
11. Matplotlib in Python: https://youtu.be/5aRtejQimtw

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Pandas provides various methods for handling missing data in DataFrames. Missing data is often represented as NaN (Not a Number) or None in Pandas. Here are some common techniques to handle missing data:

1. Detecting Missing Data:
You can use the isna() or isnull() methods to identify missing values in a DataFrame.
df.isna() # Returns a DataFrame with True where values are missing
df.isnull() # Same as isna(), returns a DataFrame with True where values are missing
df.notna() # Returns a DataFrame with True where values are not missing
df.notnull() # Same as notna(), returns a DataFrame with True where values are not missing

2. Dropping Missing Data:
You can use the dropna() method to remove rows or columns that contain missing values.
df.dropna() # Drops rows with any missing values
df.dropna(axis=1) # Drops columns with any missing values
df.dropna(thresh=2) # Drops rows that have less than 2 non-missing values.

3. Filling Missing Data:
You can use the fillna() method to fill missing values with a specific value or strategy.
df.fillna(value) # Fills missing values with a specific scalar value
df.fillna(method='ffill') # Forward fills missing values with the previous value
df.fillna(method='bfill') # Backward fills missing values with the next value
df.fillna(df.mean()) # Fills missing values with the mean of each column.

4. Replacing Missing Data:
You can use the replace() method to replace specific values in the DataFrame, including missing values.
df.replace(to_replace=np.nan, value=0) # Replaces NaN values with 0
df.replace(to_replace=[np.nan, 'None'], value='Missing') # Replaces NaN and 'None' with 'Missing'

These are just a few examples of handling missing data in Pandas. You can explore more methods and options in the official Pandas documentation: https://pandas.pydata.org/docs/user_guide/missing_data.html

Interpolation--
? It is a technique mostly used to impute missing values in the data frame or series while preprocessing data.
? We can use Interpolation to find missing value/null with the help of its neighbors.
? Interpolation is mostly used while working with time-series data because, in time-series data, we like to fill missing values with the previous one or two values. for example, suppose temperature, now we would always prefer to fill today’s temperature with the mean of the last 2 days, not with the mean of the month.
--To interpolate the missing values
df.interpolate(method ='linear', limit_direction ='forward')