Pandas Cheat Sheet for Data Science in Python | DataCamp

Series A one-dimensional labeled array 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', 'Brasilia'], 'Population': [11190846, 1303171035, 207847528]} df = pd.DataFrame(data,columns=['Country', 'Capital', 'Population'])   Country Capital Population 1 Belgium Brussels 11190846 2 India New Delhi 1303171035 3 Brazil Brasilia 207847528 Please note that the first column 1,2,3 is the index and Country,Capital,Population are the Columns. Asking For Help help(pd.Series.loc) I/O Read and Write to CSV pd.read_csv('file.csv', header=None, nrows=5) df.to_csv('myDataFrame.csv') Read multiple sheets from the same file xlsx = pd.ExcelFile('file.xls') df = pd.read_excel(xlsx, 'Sheet1') Read and Write to Excel pd.read_excel('file.xlsx') df.to_excel('dir/myDataFrame.xlsx', sheet_name='Sheet1') Read and Write to SQL Query or Database Table (read_sql()is a convenience wrapper around read_sql_table() and read_sql_query()) from sqlalchemy import create_engine engine = create_engine('sqlite:///:memory:') pd.read_sql(SELECT * FROM my_table;, engine) pd.read_sql_table('my_table', engine) pd.read_sql_query(SELECT * FROM my_table;', engine) df.to_sql('myDf', engine) Selection Getting Get one element s['b'] -5 Get subset of a DataFrame df[1:] Country Capital Population 1 India New Delhi 1303171035 2 Brazil Brasilia 207847528 Selecting', Boolean Indexing and Setting By Position Select single value by row and and column df.iloc([0], [0]) 'Belgium' df.iat([0], [0]) 'Belgium' By Label Select single value by row and column labels df.loc([0], ['Country']) 'Belgium' df.at([0], ['Country']) 'Belgium' By Label/Position Select single row of subset of rows df.ix[2] Country Brazil Capital Brasilia Population 207847528 Select a single column of subset of columns df.ix[:, 'Capital'] 0 Brussels 1 New Delhi 2 Brasilia Select rows and columns df.ix[1, 'Capital'] 'New Delhi' Boolean Indexing Series s where value is not >1 s[~(s > 1)] s where value is <-1 or >2 s[(s < -1) | (s > 2)] Use filter to adjust DataFrame df[df['Population']>1200000000] Setting Set index a of Series s to 6 s['a'] = 6 Dropping Drop values from rows (axis=0) s.drop(['a', 'c']) Drop values from columns(axis=1) df.drop('Country', axis=1) Sort and Rank Sort by labels along an axis df.sort_index() Sort by the values along an axis df.sort_values(by='Country') Assign ranks to entries df.rank() Retrieving Series/DataFrame Information Basic Information (rows, columns) df.shape Describe index df.index Describe DataFrame columns df.columns Info on DataFrame df.info() Number of non-NA values df.count() Summary Sum of values df.sum() Cumulative sum of values df.cumsum() Minimum/maximum values df.min()/df.max() Minimum/Maximum index value df.idxmin()/df.idxmax() Summary statistics df.describe() Mean of values df.mean() Median of values df.median() Applying Functions f = lambda x: x*2 Apply function df.apply(f) Apply function element-wise df.applynap(f) Internal Data Alignment NA values are introduced in the indices that don't overlap: s3 = pd.Series([7, -2, 3], index=['a', 'c', 'd']) s + s3 a 10.0 b NaN c 5.0 d 7.0 Arithmetic Operations with Fill Methods You can also do the internal data alignment yourself with the help of the fill methods: s.add(s3, fill_value=0) a 10.0 b -5.0 c 5.0 d 7.0 s.sub(s3, fill_value=2) s.div(s3, fill_value=4) s.mul(s3, fill_value=3) Learn more about pandas Joining Data with pandas Reshaping Data with pandas Data Manipulation with pandas Writing Efficient Code with pandas