Podcast
Questions and Answers
What function is used to find missing values in a DataFrame?
What function is used to find missing values in a DataFrame?
NaN is equal to itself.
NaN is equal to itself.
False (B)
Which function in NumPy checks if an element is NaN?
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.______().
To replace missing values in a DataFrame, you can use the function df.______().
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Match the following Pandas functions with their purposes:
Match the following Pandas functions with their purposes:
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Which of the following statements is correct about np.nan?
Which of the following statements is correct about np.nan?
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Pandas uses None to represent missing values in DataFrames.
Pandas uses None to represent missing values in DataFrames.
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What command would you use to remove all rows with missing values from a DataFrame?
What command would you use to remove all rows with missing values from a DataFrame?
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What is the purpose of the 'pd.melt' function in the given content?
What is the purpose of the 'pd.melt' function in the given content?
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The 'Profit' column in the melted DataFrame contains values from both 'New' and 'Old' models.
The 'Profit' column in the melted DataFrame contains values from both 'New' and 'Old' models.
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What are the identifier columns used in the 'pd.melt' function?
What are the identifier columns used in the 'pd.melt' function?
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Match the following DataFrame components with their description:
Match the following DataFrame components with their description:
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What will be the output of the line 'pd.melt(df, id_vars=Type, value_vars=[New, Old])'?
What will be the output of the line 'pd.melt(df, id_vars=Type, value_vars=[New, Old])'?
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Values from the 'New' column of the original DataFrame are repeated for each corresponding 'Old' value in the melted DataFrame.
Values from the 'New' column of the original DataFrame are repeated for each corresponding 'Old' value in the melted DataFrame.
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Which method can be used to impute missing data?
Which method can be used to impute missing data?
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The outcome of the dropna function is to drop rows that contain NaN values.
The outcome of the dropna function is to drop rows that contain NaN values.
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What is the primary purpose of scaling and standardization in data preprocessing?
What is the primary purpose of scaling and standardization in data preprocessing?
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Data can be encoded as _____ vectors using one-of-K encoding.
Data can be encoded as _____ vectors using one-of-K encoding.
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Match the following terms with their definitions:
Match the following terms with their definitions:
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Which of the following libraries is scikit-learn built on top of?
Which of the following libraries is scikit-learn built on top of?
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Scikit-learn requires data to be in a format other than Numpy or Pandas DataFrame.
Scikit-learn requires data to be in a format other than Numpy or Pandas DataFrame.
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What happens when performing arithmetic operations on a DataFrame that contains NaN values?
What happens when performing arithmetic operations on a DataFrame that contains NaN values?
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Using the method dropna with axis set to 0 will remove columns that contain NaN values.
Using the method dropna with axis set to 0 will remove columns that contain NaN values.
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What method is used to fill NaN values in a DataFrame with the mean of a column?
What method is used to fill NaN values in a DataFrame with the mean of a column?
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The command df.dropna(axis=0)
will remove the rows that have __________ values.
The command df.dropna(axis=0)
will remove the rows that have __________ values.
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Match the following pandas methods with their descriptions:
Match the following pandas methods with their descriptions:
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What is the purpose of the command df.fillna(df['Num'].mean())
?
What is the purpose of the command df.fillna(df['Num'].mean())
?
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What will the command df['Num'].sum()
return if there are missing values in the 'Num' column?
What will the command df['Num'].sum()
return if there are missing values in the 'Num' column?
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Mathematical operators in Pandas will ignore NaN values.
Mathematical operators in Pandas will ignore NaN values.
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What function can replace missing values in a DataFrame with the mean of a specific column?
What function can replace missing values in a DataFrame with the mean of a specific column?
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To drop rows with missing values in a DataFrame, you would use the function df.dropna(axis=__)
.
To drop rows with missing values in a DataFrame, you would use the function df.dropna(axis=__)
.
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Which of the following is NOT a way to handle missing values in Pandas?
Which of the following is NOT a way to handle missing values in Pandas?
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Match the missing value terms with their meanings:
Match the missing value terms with their meanings:
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Numpy can perform operations on non-numerical missing data types like 'None'.
Numpy can perform operations on non-numerical missing data types like 'None'.
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What binary value will be produced for an input of 0.5 using the Binarizer with a threshold of 0.6?
What binary value will be produced for an input of 0.5 using the Binarizer with a threshold of 0.6?
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What happens if you attempt to sum a column in a DataFrame containing NaN using only NumPy functions?
What happens if you attempt to sum a column in a DataFrame containing NaN using only NumPy functions?
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The Binarizer function can take any threshold value, not just 0.6.
The Binarizer function can take any threshold value, not just 0.6.
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What is the purpose of encoding categorical features as integers?
What is the purpose of encoding categorical features as integers?
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The encoded size values are: 'S' = ______, 'M' = ______, 'L' = ______.
The encoded size values are: 'S' = ______, 'M' = ______, 'L' = ______.
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ML models only work with boolean input values.
ML models only work with boolean input values.
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What is the output of the Binarizer when the input is greater than 0.6?
What is the output of the Binarizer when the input is greater than 0.6?
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Flashcards
Pandas DataFrame
Pandas DataFrame
A two-dimensional labeled data structure with columns of potentially different types.
Melting a DataFrame
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
id_vars in melt
Columns in the original DataFrame that will be preserved as identifiers in the reshaped DataFrame.
value_vars in melt
value_vars in melt
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Pivot (Reshape) Operation
Pivot (Reshape) Operation
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Column Manipulation
Column Manipulation
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Data Conversion (pd.DataFrame)
Data Conversion (pd.DataFrame)
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var_name in melt
var_name in melt
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Missing Values in Pandas
Missing Values in Pandas
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Pandas isna()
Pandas isna()
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Pandas fillna()
Pandas fillna()
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Pandas dropna()
Pandas dropna()
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Numpy isnan()
Numpy isnan()
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NaN equality
NaN equality
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Regular Expressions (re)
Regular Expressions (re)
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Pandas replace()
Pandas replace()
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Pandas Math with Missing Values
Pandas Math with Missing Values
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Handling Missing Values in Calculations
Handling Missing Values in Calculations
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How Pandas Identifies Missing Values
How Pandas Identifies Missing Values
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NumPy's Behavior with Missing Values
NumPy's Behavior with Missing Values
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Why does it show True/False?
Why does it show True/False?
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Ignoring NaN in Calculations
Ignoring NaN in Calculations
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Handling Missing Values in Pandas
Handling Missing Values in Pandas
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Filling Missing Values (fillna)
Filling Missing Values (fillna)
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.fillna()
with a Value
.fillna()
with a Value
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.fillna()
with Mean
.fillna()
with Mean
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Dropping Rows with NaN (dropna
)
Dropping Rows with NaN (dropna
)
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Dropping Rows (axis=0
)
Dropping Rows (axis=0
)
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.dropna()
with axis=1
.dropna()
with axis=1
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.isnull()
.isnull()
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Imputing Missing Data
Imputing Missing Data
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Mean/Median Imputation
Mean/Median Imputation
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Most Frequent Imputation
Most Frequent Imputation
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Data Scaling
Data Scaling
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Standardization
Standardization
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L1/L2 Normalization
L1/L2 Normalization
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Binarization
Binarization
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Encoding Categorical Features
Encoding Categorical Features
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Binarizer Threshold
Binarizer Threshold
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Why Binarize?
Why Binarize?
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LabelEncoder
LabelEncoder
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One-Hot Encoding
One-Hot Encoding
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Why Encode Categorical Features?
Why Encode Categorical Features?
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Numerical Input for ML
Numerical Input for ML
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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
, andProfit
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
).
- NumPy mathematical functions propagate missing values as
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
(fromsklearn.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)
- Use
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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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.