fixed-frequency) time series data. • Arbitrary matrix data (homogeneously typed or heterogeneous) with row and column labels • Any other form of observational / statistical data sets. The data actually need SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame. Try using .loc[row_index,col_indexer] = value instead Here is the correct method of assignment. In [8]: dfc.loc[0,’A’] Out[87]: 0 1 0 a 1 1 b 2 2 NaN NaN [3 rows x 2 columns] Elements that do not match return a row of NaN. Thus, a Series of messy strings can be converted into a like- indexed Series or DataFrame of

fixed-frequency) time series data. • Arbitrary matrix data (homogeneously typed or heterogeneous) with row and column labels • Any other form of observational / statistical data sets. The data actually need (GH554) • Implement DataFrame.lookup, fancy-indexing analogue for retrieving values given a sequence of row and column labels (GH338) • Can pass a list of functions to aggregate with groupby on a DataFrame 85625373985124742 This is all exactly identical to the behavior before. However, if you ask for a key not contained in the Series, in versions 0.6.1 and prior, Series would fall back on a location-based

fixed-frequency) time series data. • Arbitrary matrix data (homogeneously typed or heterogeneous) with row and column labels • Any other form of observational / statistical data sets. The data actually need implemented for bool dtypes • In HDFStore, select_as_multiple will always raise a KeyError, when a key or the selector is not found (GH6177) • df[’col’] = value and df.loc[:,’col’] = value are now completely 10:00:00 2013-09-05 10:00:00 1 In [78]: pivot_table(df, index=Grouper(freq=’M’, key=’Date’), ....: columns=Grouper(freq=’M’, key=’PayDay’), ....: values=’Quantity’, aggfunc=np.sum) ....: Out[78]: PayDay

fixed-frequency) time series data. • Arbitrary matrix data (homogeneously typed or heterogeneous) with row and column labels • Any other form of observational / statistical data sets. The data actually need (GH554) • Implement DataFrame.lookup, fancy-indexing analogue for retrieving values given a sequence of row and column labels (GH338) • Can pass a list of functions to aggregate with groupby on a DataFrame 85625373985124742 This is all exactly identical to the behavior before. However, if you ask for a key not contained in the Series, in versions 0.6.1 and prior, Series would fall back on a location-based

fixed-frequency) time series data. • Arbitrary matrix data (homogeneously typed or heterogeneous) with row and column labels • Any other form of observational / statistical data sets. The data actually need (GH554) • Implement DataFrame.lookup, fancy-indexing analogue for retrieving values given a sequence of row and column labels (GH338) • Can pass a list of functions to aggregate with groupby on a DataFrame 2963101333219374 This is all exactly identical to the behavior before. However, if you ask for a key not contained in the Series, in versions 0.6.1 and prior, Series would fall back on a location-based

tutorials. To the getting started guides User guide The user guide provides in-depth information on the key concepts of pandas with useful background information and explanation. To the user guide API reference the methods work and which parameters can be used. It assumes that you have an understanding of the key concepts. To the reference guide Developer guide Saw a typo in the documentation? Want to improve To user guide Straight to tutorial... Multiple tables can be concatenated both column wise and row wise as database-like join/merge operations are provided to combine multiple tables of data. To introduction

tutorials. To the getting started guides User guide The user guide provides in-depth information on the key concepts of pandas with useful background information and explanation. To the user guide API reference the methods work and which parameters can be used. It assumes that you have an understanding of the key concepts. To the reference guide Developer guide Saw a typo in the documentation? Want to improve To user guide Straight to tutorial... Multiple tables can be concatenated both column wise and row wise as database-like join/merge operations are provided to combine multiple tables of data. To introduction

tutorials. To the getting started guides User guide The user guide provides in-depth information on the key concepts of pandas with useful background information and explanation. To the user guide API reference the methods work and which parameters can be used. It assumes that you have an understanding of the key concepts. To the reference guide Developer guide Saw a typo in the documentation? Want to improve To user guide Straight to tutorial... Multiple tables can be concatenated both column wise and row wise as database-like join/merge operations are pro- vided to combine multiple tables of data. To

fixed-frequency) time series data. • Arbitrary matrix data (homogeneously typed or heterogeneous) with row and column labels • Any other form of observational / statistical data sets. The data actually need DataFrame({'key': ['foo', 'foo'], 'lval': [1, 2]}) In [78]: right = pd.DataFrame({'key': ['foo', 'foo'], 'rval': [4, 5]}) In [79]: left Out[79]: key lval 0 foo 1 1 foo 2 In [80]: right Out[80]: key rval 0 [81]: pd.merge(left, right, on='key') Out[81]: key lval rval 0 foo 1 4 1 foo 1 5 2 foo 2 4 3 foo 2 5 Another example that can be given is: In [82]: left = pd.DataFrame({'key': ['foo', 'bar'], 'lval': [1