Pandas Data Structure
Series |
A one-dimensional labeled array a capable of holding any data type |
>>> s = pd.Series([3, -5, 7, 4], index=['a', 'b', 'c', 'd']) |
DataFrame |
A two-dimensional labeled data structure with columns of potentially different types |
>>> data = {'Country': ['Belgium', 'India', 'Brazil'], 'Capital': ['Brussels', 'New Delhi', 'Brasília'], 'Population': [11190846, 1303171035, 207847528]} |
I/O
Read and Write to CSV |
>>> pd.read_csv( , header=None, nrows=5) |
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>>> df.to_csv('myDataFrame.csv') |
Read and Write to Excel |
>>> pd.read_excel( ) |
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>>> df.to_excel('dir/myDataFrame.xlsx', sheet_name='Sheet1') |
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Applying Functions
Apply function element-wise |
>>> f = lambda x: x*2 |
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>>> df.apply(f) |
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>>> df.applymap(f) |
Basic Information
(rows,columns) |
>>> df.shape |
Describe index |
>>> df.index |
Describe DataFrame columns |
>>> df.columns |
Info on DataFrame |
>>> df.info() |
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Data Alignment
NA values are introduced in the indices that don’t overlap: |
>>> s3 = pd.Series([7, -2, 3], index=['a', 'c', 'd']) |
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>>> s + s3 a 10.0 b NaN c 5.0 d 7.0 |
Data Alignment
NA values are introduced in the indices that don’t overlap: |
>>> s3 = pd.Series([7, -2, 3], index=['a', 'c', 'd']) |
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>>> s + s3 a 10.0 b NaN c 5.0 d 7.0 |
Data Alignment
NA values are introduced in the indices that don’t overlap: |
>>> s3 = pd.Series([7, -2, 3], index=['a', 'c', 'd']) |
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>>> s + s3 a 10.0 b NaN c 5.0 d 7.0 |
Data Alignment
NA values are introduced in the indices that don’t overlap: |
>>> s3 = pd.Series([7, -2, 3], index=['a', 'c', 'd']) |
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>>> s + s3 a 10.0 b NaN c 5.0 d 7.0 |
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