Data rarely arrives in a pristine, ready-to-analyze format. Data cleaning involves identifying and correcting errors, inconsistencies, and missing values. Data transformation converts data into a suitable format for analysis. This process is vital for ensuring accurate results and meaningful insights. Think of it like preparing ingredients before cooking – you need to chop vegetables, measure spices, and maybe even rinse the rice before you can start making your meal.

Missing values (represented as NaN in Pandas) are common in datasets. They can arise from various reasons like data entry errors, sensor failures, or simply unavailable information. Ignoring missing values can lead to biased results. Here's how we address them:

• Identifying Missing Values: Use .isnull() or .isna() methods in Pandas to detect missing values. The .sum() method, when chained to these, quickly reveals the number of missing values per column.

  ```python
  import pandas as pd

  Example DataFrame (replace with your data)

  data = {'A': [1, 2, None, 4, 5], 'B': [6, None, 8, 9, 10]}
  df = pd.DataFrame(data)

  print(df.isnull().sum())
  ```

• Handling Strategies:

  • Dropping Rows/Columns: Remove rows or columns containing missing values using .dropna(). This is suitable when missing values are few or the information in the row/column isn't critical.
    python df_dropped = df.dropna() print(df_dropped)
  • Imputation: Replace missing values with estimated values. Common methods include:

    • Mean/Median/Mode Imputation: Replace missing values with the mean, median, or mode of the column. Useful for numeric data.
      ```python
      from sklearn.impute import SimpleImputer
      import numpy as np

      imputer_mean = SimpleImputer(strategy='mean') # Or 'median', 'most_frequent'
      df['A'] = imputer_mean.fit_transform(df[['A']])
      print(df)
      ```
      * Using a Constant Value: Replace missing values with a specific constant (e.g., 0, -999) if appropriate for the data. This might be useful if the absence of a value itself carries meaning.
      * Advanced Imputation (Beyond Beginner): More sophisticated methods like k-Nearest Neighbors imputation or model-based imputation can be employed, but they are beyond the scope of this beginner lesson.

Data types (e.g., integer, float, string, boolean, datetime) determine how data is stored and manipulated. Incorrect data types can cause errors and inaccurate analysis.

• Identifying Data Types: Use the .dtypes attribute in Pandas to check the data types of each column.
  python print(df.dtypes)

• Converting Data Types: Use .astype() method for changing the data type of a column.
  ```python
  # Convert column 'A' to integer (if possible)
  df['A'] = df['A'].astype(int)
  print(df.dtypes)

  Convert column 'B' (containing strings) to datetime

  First, make sure your data are strings. If they are not (e.g., are NaN), you must fill these first.

  data = {'date_col': ['2023-01-01', '2023-01-02', '2023-01-03', None, '2023-01-05']}
  df_dates = pd.DataFrame(data)
  df_dates['date_col'] = df_dates['date_col'].fillna('2023-01-01') # Or other date-like default value, if NaN is present.
  df_dates['date_col'] = pd.to_datetime(df_dates['date_col'])
  print(df_dates.dtypes)
  `` * **Dealing with Strings/Objects:** Strings are often represented as 'object' in Pandas. You can convert object types to the correct data types where necessary usingastype()`. Be careful if the 'object' column includes numeric values; you might need to handle the conversion and missing value replacement steps separately.