Data Tidying and Preprocessing Quiz
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Questions and Answers

What function is used to find missing values in a DataFrame?

  • df.replace()
  • np.isnan()
  • df.fillna()
  • df.isna() (correct)

NaN is equal to itself.

False (B)

Which function in NumPy checks if an element is NaN?

np.isnan()

To replace missing values in a DataFrame, you can use the function df.______().

<p>fillna</p> Signup and view all the answers

Match the following Pandas functions with their purposes:

<p>df.isna() = Check for missing values df.fillna() = Replace missing values df.dropna() = Remove missing values df.replace() = Replace specified values</p> Signup and view all the answers

Which of the following statements is correct about np.nan?

<p>np.nan does not equal itself (A)</p> Signup and view all the answers

Pandas uses None to represent missing values in DataFrames.

<p>True (A)</p> Signup and view all the answers

What command would you use to remove all rows with missing values from a DataFrame?

<p>df.dropna()</p> Signup and view all the answers

What is the purpose of the 'pd.melt' function in the given content?

<p>To unpivot a DataFrame (A)</p> Signup and view all the answers

The 'Profit' column in the melted DataFrame contains values from both 'New' and 'Old' models.

<p>True (A)</p> Signup and view all the answers

What are the identifier columns used in the 'pd.melt' function?

<p>Type</p> Signup and view all the answers

Match the following DataFrame components with their description:

<p>Type = Identifier column Model = New and Old categories Profit = Values corresponding to the Type and Model pd.melt = Unpivoting function</p> Signup and view all the answers

What will be the output of the line 'pd.melt(df, id_vars=Type, value_vars=[New, Old])'?

<p>A DataFrame with melted rows for New and Old under the Model column (D)</p> Signup and view all the answers

Values from the 'New' column of the original DataFrame are repeated for each corresponding 'Old' value in the melted DataFrame.

<p>True (A)</p> Signup and view all the answers

Which method can be used to impute missing data?

<p>Using mean, median, or most frequent (A)</p> Signup and view all the answers

The outcome of the dropna function is to drop rows that contain NaN values.

<p>False (B)</p> Signup and view all the answers

What is the primary purpose of scaling and standardization in data preprocessing?

<p>To adjust the features to a similar scale for better model performance.</p> Signup and view all the answers

Data can be encoded as _____ vectors using one-of-K encoding.

<p>binary</p> Signup and view all the answers

Match the following terms with their definitions:

<p>Imputation = Filling in missing data Standardization = Scaling features to have a mean of 0 Binarization = Transforming numerical data into binary form Normalization = Scaling data to a range of [0, 1]</p> Signup and view all the answers

Which of the following libraries is scikit-learn built on top of?

<p>NumPy and Matplotlib (B)</p> Signup and view all the answers

Scikit-learn requires data to be in a format other than Numpy or Pandas DataFrame.

<p>False (B)</p> Signup and view all the answers

What happens when performing arithmetic operations on a DataFrame that contains NaN values?

<p>The result will propagate NaN. (A)</p> Signup and view all the answers

Using the method dropna with axis set to 0 will remove columns that contain NaN values.

<p>False (B)</p> Signup and view all the answers

What method is used to fill NaN values in a DataFrame with the mean of a column?

<p>fillna</p> Signup and view all the answers

The command df.dropna(axis=0) will remove the rows that have __________ values.

<p>NaN</p> Signup and view all the answers

Match the following pandas methods with their descriptions:

<p>fillna = Replaces NaN values with specified values dropna = Removes NaN values from specified axis mean = Calculates the average of numeric values DataFrame = Main data structure in pandas</p> Signup and view all the answers

What is the purpose of the command df.fillna(df['Num'].mean())?

<p>To fill NaN values in 'Num' with the mean of that column.</p> Signup and view all the answers

What will the command df['Num'].sum() return if there are missing values in the 'Num' column?

<p>The sum of the non-missing values (A)</p> Signup and view all the answers

Mathematical operators in Pandas will ignore NaN values.

<p>False (B)</p> Signup and view all the answers

What function can replace missing values in a DataFrame with the mean of a specific column?

<p>df.fillna(np.mean(df['Num']))</p> Signup and view all the answers

To drop rows with missing values in a DataFrame, you would use the function df.dropna(axis=__).

<p>0</p> Signup and view all the answers

Which of the following is NOT a way to handle missing values in Pandas?

<p>Ignoring rows entirely whenever there's a NaN (D)</p> Signup and view all the answers

Match the missing value terms with their meanings:

<p>NaN = Not a Number, typically used for missing numerical data None = A Python object that represents no value np.nan = A NumPy representation for Not a Number NA = A string representation of missing data</p> Signup and view all the answers

Numpy can perform operations on non-numerical missing data types like 'None'.

<p>False (B)</p> Signup and view all the answers

What binary value will be produced for an input of 0.5 using the Binarizer with a threshold of 0.6?

<p>0 (B)</p> Signup and view all the answers

What happens if you attempt to sum a column in a DataFrame containing NaN using only NumPy functions?

<p>The result will be NaN.</p> Signup and view all the answers

The Binarizer function can take any threshold value, not just 0.6.

<p>True (A)</p> Signup and view all the answers

What is the purpose of encoding categorical features as integers?

<p>Many operations only work with numerical values.</p> Signup and view all the answers

The encoded size values are: 'S' = ______, 'M' = ______, 'L' = ______.

<p>1, 2, 3</p> Signup and view all the answers

ML models only work with boolean input values.

<p>False (B)</p> Signup and view all the answers

What is the output of the Binarizer when the input is greater than 0.6?

<p>1</p> Signup and view all the answers

Flashcards

Pandas DataFrame

A two-dimensional labeled data structure with columns of potentially different types.

Melting a DataFrame

Reshaping a table from wide format to long format. It takes columns as variables, combining them into a single column of values and a column for their original name.

id_vars in melt

Columns in the original DataFrame that will be preserved as identifiers in the reshaped DataFrame.

value_vars in melt

Columns in the original DataFrame that will be combined into a single column of values in the reshaped DataFrame.

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Pivot (Reshape) Operation

Converting a dataset with multiple columns into a format with one or more columns, often to summarize data.

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Column Manipulation

Actions like selection, addition, or deletion of columns in a DataFrame.

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Data Conversion (pd.DataFrame)

Creating a Pandas DataFrame from data like dictionaries or lists.

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var_name in melt

The name of the column created by "melting" that will store the original column name.

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Missing Values in Pandas

Representing absent or unknown data in a Pandas DataFrame. Common representations include NaN (Not a Number), None, empty cells, or incorrect data.

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Pandas isna()

A Pandas method used to identify missing values (NaN, None) within a DataFrame or Series.

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Pandas fillna()

A Pandas method used to replace missing values within a DataFrame or Series. You can specify a replacement value.

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Pandas dropna()

A Pandas method used to remove rows or columns containing missing values (NaN, None) from a DataFrame.

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Numpy isnan()

A NumPy function used to identify missing values (NaN) within a NumPy array (Float arrays only).

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NaN equality

NaN values in Python are not equal to themselves. Use specialized functions like np.isnan() or df.isna() to check for them.

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Regular Expressions (re)

Powerful tools for pattern matching in text data.

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Pandas replace()

A Pandas method used to find and replace specific values in a DataFrame or Series using a direct substitution method.

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Pandas Math with Missing Values

In Pandas, mathematical functions like sum() automatically ignore missing values (NaN) and treat them as 0. However, mathematical operators like + propagate NaN, resulting in NaN for the entire operation.

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Handling Missing Values in Calculations

To handle missing values in calculations and avoid NaN propagation, replace them with a specific value (like the mean) using df.fillna() or drop rows/columns with missing values using df.dropna().

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How Pandas Identifies Missing Values

Pandas recognizes various representations of missing values, including np.nan, 'NA', 'NaN', and None.

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NumPy's Behavior with Missing Values

NumPy, unlike Pandas, cannot handle non-numerical data like 'NA', 'NaN', or None. Operations on data with missing values in NumPy will result in NaN.

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Why does it show True/False?

When examining a DataFrame for missing values, using the isnull() method will return a DataFrame with Boolean values (True/False) indicating the presence or absence of missing values in each cell.

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Ignoring NaN in Calculations

Functions like sum() in Pandas are designed to handle missing values and ignore them automatically, providing a more robust and straightforward way to perform calculations on data with missing values.

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Handling Missing Values in Pandas

Managing NaN (Not a Number) values in Pandas DataFrames using various techniques.

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Filling Missing Values (fillna)

Replacing NaN values with specified values or using statistical methods like mean or median.

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.fillna() with a Value

Replaces all missing values with a specific value.

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.fillna() with Mean

Replaces NaN values with the mean value of the column.

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Dropping Rows with NaN (dropna)

Removes rows from a DataFrame that contain NaN values.

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Dropping Rows (axis=0)

Specifies that rows should be dropped in the dropna function.

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.dropna() with axis=1

Removes columns from a DataFrame that contain NaN values.

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.isnull()

Checks if a value is missing (NaN). Returns True for missing values and False otherwise.

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Imputing Missing Data

The process of replacing missing values in a dataset with estimated values.

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Mean/Median Imputation

Replacing missing values with the mean or median of the existing values in the same column.

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Most Frequent Imputation

Replacing missing values with the most frequently occurring value in the same column.

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Data Scaling

Adjusting the range of data values to a common scale, often between 0 and 1.

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Standardization

Transforming data to have a mean of 0 and a standard deviation of 1.

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L1/L2 Normalization

Rescaling data to have a unit norm (L1 or L2).

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Binarization

Converting continuous data into binary (0 or 1) values based on a threshold.

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Encoding Categorical Features

Converting categorical data into numerical representations for use in machine learning models.

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Binarizer Threshold

The value that determines the cutoff point for binarization. Values below the threshold are converted to 0, and values above are converted to 1.

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Why Binarize?

Some machine learning models require binary input values. Binarization also helps with boolean questions, converting continuous probabilities to clear yes/no answers.

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LabelEncoder

A scikit-learn tool to automatically convert unique categorical labels to corresponding integers.

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One-Hot Encoding

Transforming categorical features into binary vectors. Each unique category becomes a separate column, with 1 indicating presence and 0 indicating absence.

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Why Encode Categorical Features?

Many machine learning algorithms require numerical input. Encoding converts categorical data (like countries or sizes) into a format they can understand.

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Numerical Input for ML

Many machine learning models and algorithms require numerical data. This data can be raw numerical values or encoded categorical features.

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Study Notes

Data Tidying and Data Preprocessing

  • Data moves from raw sources (databases, web, Excel, text files, APIs) to tidying, then to tabular data.
  • Tidying arranges data for analysis, using techniques like split-apply-combine, groupby, melt, and pivot.
  • Preprocessing transforms raw data into a format suitable for analysis or machine learning.
  • Data analysis uses statistics and visualizations to understand data, including machine learning and optimization.
  • 80% of work is tidying/pre-processing, 20% is analysis
  • A dataset is a collection of numerical and/or categorical values.
  • A variable groups values measuring the same attribute, criteria, or feature. All values share the same type and units.
  • An observation groups the values of multiple variables for a single object, person, item, etc.
  • Example data shows a table with Type, Model, and Profit columns. This illustrates 3 variables and 6 observations, representing sales profit for different models.
  • Long format data has one column per variable. Wide format has more than one column containing the same variable. Long format is better for machine learning.
  • Tidy data properties:
    • Each variable is in a single column.
    • Each row is a complete observation.
    • Each table represents a type of observational unit.
  • Wide format to long format (melt/unpivot). Long format to wide format (pivot).
  • Pandas library is a Python library for data manipulation. The Website: http://pandas.pydata.org/ and Documentation: http://pandas.pydata.org/pandas-docs/stable/
  • import pandas as pd
  • Data Wrangling/Tidying arranges data for analysis, using techniques like Split-Apply-Combine, groupby, melt, pivot etc..
  • Data Munging/Preprocessing transforms raw data into an appropriate format for data analysis or machine learning.
  • Data Analysis uses statistics and visualizations to understand data, often using machine learning, optimization, etc.
  • Common Data Preprocessing steps: Textual transformations (e.g., using regular expressions), handling missing values (dropping, replacing), scaling, standardization, binarization, encoding categorical features (as integers, binary vectors).

Missing Values

  • Missing data can include unknown, lost, wrong data or empty cells in Excel/CSV files. Incorrect indexing often causes missing values (NaN).
  • Missing values (NaN) are handled differently in NumPy and Pandas
    • NumPy mathematical functions propagate missing values as NaN.
    • Special NumPy functions, like np.nansum(), ignore missing values.
    • Replace missing values with a particular value in NumPy (e.g, 0).
    • Handle missing values in Pandas using methods like df.isna(), df.fillna(), df.dropna(). Use .mean() to calculate missing values based on the average for columns or rows (axis = 0 or 1).

Scaling (MinMaxScaler)

  • Scaling data to a specific range (e.g., [0, 1]) is often necessary because different ranges across columns can affect results.
  • MinMaxScaler scales data to a specified range (e.g., 0,1).
  • This normalization is useful in many machine learning algorithms.

Standardisation

  • Standardisation, also called Z-score normalization, transforms data to a normal distribution with a mean of 0 and a standard deviation of 1.
  • Useful when models assume data is centered around 0 (e.g., RBF kernel in support vector machines (SVM)) or when features have very different variances.

L1/L2 Normalisation

  • Normalises individual samples (rows) to have a unit norm of 1.
  • L1 and L2 are standard unit norms for comparing similarity/distance between samples in vector spaces.

Binarisation

  • Convert numerical values to binary (0, 1) based on a threshold.
  • Helpful for some machine learning models requiring boolean input values

Encoding Categorical Features

  • Encode categorical data into numerical values for many data processing operations.
    • Use LabelEncoder (from sklearn.preprocessing) to encode data (e.g., US=0,ES=1, UK=2).
    • Use OneHotEncoder to create one-hot encoded variables (e.g, CN, ES, UK, US represented as independent binary features in the new columns)

Split-Apply-Combine

  • Split data based on a common criteria, or the index (row).
  • Apply an operation/calculations to each group
  • Combine to get results

Groupby

  • .groupby() (on a specific column) is not a standard dataframe.
  • groupedby_type creates a separate dataframe for each unique value of the groupby column.

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Data Tidying and Preprocessing Lecture Notes PDF

Description

Explore the essential concepts of data tidying and preprocessing in this quiz. Understand the techniques used for arranging and transforming data for effective analysis and machine learning. Test your knowledge on the differences between variables and observations as well as the importance of each step in data analysis.

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