Cheatography
https://cheatography.com
This cheat sheet provides a concise reference guide for transitioning between NumPy and Pandas, two essential libraries in Python for data manipulation and analysis. It outlines the relationship between the two libraries and offers fundamental operations for handling data effectively.
This is a draft cheat sheet. It is a work in progress and is not finished yet.
Create Objects
Creating a NumPy array |
arr = np.array([1, 2, 3, 4, 5])
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Creating a Pandas DataFrame |
df = pd.DataFrame({'A': [1, 2, 3], 'B': [4, 5, 6]})
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Indexing and Selection:
Indexing and slicing NumPy array |
arr[0]
Access element at index 0 |
arr[2:4]
Slice elements from index 2 to 4 |
Indexing and slicing Pandas DataFrame |
df['A']
Access column 'A' |
df.loc[0]
Access row at label 0 |
df.iloc[0]
Access row at index 0 |
Operations
Arithmetic operations with NumPy arrays |
np.sum(arr)
Sum of all elements |
np.mean(arr)
Mean of all elements |
Arithmetic operations with Pandas DataFrame |
df.sum()
Sum of all elements (column-wise) |
df.mean()
Mean of all elements (column-wise) |
Missing Data Handling
Numpy |
NumPy does not have built-in support for missing data |
Handling missing data in Pandas DataFrame |
df.isnull()
Detect missing values |
df.dropna()
Drop rows with missing values |
df.fillna(value)
Fill missing values with specified value |
Grouping
Grouping data in a Pandas DataFrame and calculates the Mean value for each group |
df.groupby('column_name').mean()
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Grouping data in a Pandas DataFrame and calculates the Sum of values for each group |
df.groupby('column_name').sum()
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Aggregation
Aggregating data in a Pandas DataFrame |
df.groupby('column_name').agg({'column_to_aggregate': 'aggregate_function'})
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Example: Sum of 'values' column for each 'category' |
df.groupby('category').agg({'values': 'sum'})
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Filtering
Filtering data in a Pandas DataFrame |
df[df['column_name'] > threshold]
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Example: Filtering rows where 'values' column is greater than 10 |
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