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Machine Learning Code Snippets: June 2018
Useful code snippets, functions, and classes that will make you more efficient with Pandas, Numpy, and Jupyter Notebooks.
This blog post contains a number of useful code snippets, functions, and classes that will help with machine learning in Jupyter Notebooks. Specific usage instructions as well as dependencies
Modifying and Selecting Information from a DataFrame
Random Sample of Rows
Choose a random sample of rows from a large dataframe. This is very useful when trying to reduce your training set for debugging purposes.
indices = np.random.randint(10, size=2) smallerDF = df[indices,:]
Selecting and Removing Columns
It’s very easy to extract or remove columns from a pandas dataframe using their built in indexing actions.
# Extract these keys into their own data frame preserveKeys = ['x', 'y'] smallDF = df[preserveKeys] # Create a new dataframe without certain columns newDF = df.drop(columns=['z'])
Converting All Non-Null Entries to 1
This is helpful when converting a dataframe into a boolean dataframe in which a 1 indicates the prescence of a value and a null indicates there was no value. It can turn any dataframe into a sort of “checkbox” which is helpful for certain types of data processing like collaborative filtering where the actual value doesn’t matter.
booleanDF = copy.deepcopy(df) # Deep copy so don't modify other DF # Convert 'np.nan' to 0's and everything else to 1's booleanDF = booleanDF.notnull().astype('int') # Replace all 0's with 'np.nan' booleanDF = booleanDF.replace(0, np.nan)
# Merge 2 dataframes with the same rows (ie. add new columns) finalDF = pd.concat([df1, df2], sort=True) # Merge 2 dataframes with the same columns (ie. add more rows) finalDF = pd.concat([df1, df2], sort=True, axis=0) # Merge 2 dataframes by row and add new columns when appropriate finalDF = pd.concat([df1, df2], sort=True, axis=0, ignore_index=True)
Getting an Overview of a DataFrame (Make this into another
It’s often difficult to deal with abstract, seemingly black-box machine learning algorithms. What can help alleviate some of this stress is knowing what your data really looks like. Here’s a few examples that will help you understand what’s going on in your dataset.
Printing Basic Excerpts
df.describe() df.head(5) # First 5 rows
df.describe() will print a table of all columns and their respective counts (how many non-null values in the column), mean, std (standard deviation), min, 25%, 50%, 75%, and max. Sometimes the row name doens’t get included. This can be fixed by passing in the argument “include=’all’” as follows:
df.head(n) will print the first n rows of your dataset and can give you a good understanding of the form of your data. While the
describe() function is good at showing you basic distributions, the
head() function will show you what your data actually looks like quickly and easily.
Posted in General Programming with Python, Source Code