identifiers like names that start with a digit, are python keywords, or are using single character operators. (GH27017) • Bug in pd.core.util.hashing.hash_pandas_object where arrays containing tuples were 744366 2000-01-07 0.353673 0.003831 0.072007 2000-01-08 0.000323 34.531275 0.354386 Boolean operators work as well: In [95]: df1 = pd.DataFrame({'a': [1, 0, 1], 'b': [0, 1, 1]}, dtype=bool) In [96]: name in a MultiIndex, with default name level_0, level_1, ... if not provided. Valid comparison operators are: =, ==, !=, >, >=, <, <= Valid boolean expressions are combined with: • | : or • & : and

methods which require numeric index. (GH21662) • Bug in eval() when comparing floats with scalar operators, for example: x < -0.1 (GH25928) • Fixed bug where casting all-boolean array to integer extension 2000-01-07 3.843884 0.152958 6.915104e-07 2000-01-08 0.202928 0.002833 2.461896e-02 Boolean operators work as well: In [97]: df1 = pd.DataFrame({'a': [1, 0, 1], 'b': [0, 1, 1]}, dtype=bool) In [98]: • if data_columns are specified, these can be used as additional indexers. Valid comparison operators are: =, ==, !=, >, >=, <, <= Valid boolean expressions are combined with: • | : or • & : and

methods which require numeric index. (GH21662) • Bug in eval() when comparing floats with scalar operators, for example: x < -0.1 (GH25928) • Fixed bug where casting all-boolean array to integer extension 0.399202 2000-01-07 0.000033 2.379250 0.007327 2000-01-08 0.009758 0.011567 0.031388 Boolean operators work as well: 152 Chapter 3. Getting started pandas: powerful Python data analysis toolkit, Release • if data_columns are specified, these can be used as additional indexers. Valid comparison operators are: =, ==, !=, >, >=, <, <= Valid boolean expressions are combined with: • | : or • & : and

is made in one go and just using selection brackets [] is not sufficient anymore. The loc/iloc operators are required in front of the selection brackets []. When using loc/iloc, the part before the comma element-wise, so the / is applied for the values in each row. Also other mathematical operators (+, -, *, /) or logical operators (<, >, =,...) work element wise. The latter was already used in the subset data 008277 2000-01-07 22.579530 3.521204 0.829033 2000-01-08 4.577374 9.233151 0.466834 Boolean operators work as well: In [95]: df1 = pd.DataFrame({'a': [1, 0, 1], 'b': [0, 1, 1]}, dtype=bool) In [96]:

is made in one go and just using selection brackets [] is not sufficient anymore. The loc/iloc operators are required in front of the selection brackets []. When using loc/iloc, the part before the comma element-wise, so the / is applied for the values in each row. Also other mathematical operators (+, -, *, /) or logical operators (<, >, =,...) work element wise. The latter was already used in the subset data 008277 2000-01-07 22.579530 3.521204 0.829033 2000-01-08 4.577374 9.233151 0.466834 Boolean operators work as well: In [95]: df1 = pd.DataFrame({'a': [1, 0, 1], 'b': [0, 1, 1]}, dtype=bool) In [96]:

is made in one go and just using selection brackets [] is not sufficient anymore. The loc/iloc operators are required in front of the selection brackets []. When using loc/iloc, the part before the comma element-wise, so the / is applied for the values in each row. Also other mathematical operators (+, -, *, /) or logical operators (<, >, =,...) work element wise. The latter was already used in the subset data 008277 2000-01-07 22.579530 3.521204 0.829033 2000-01-08 4.577374 9.233151 0.466834 Boolean operators work as well: In [98]: df1 = pd.DataFrame({'a': [1, 0, 1], 'b': [0, 1, 1]}, dtype=bool) In [99]:

is made in one go and just using selection brackets [] is not sufficient anymore. The loc/iloc operators are required in front of the selection brackets []. When using loc/iloc, the part before the comma element-wise, so the / is applied for the values in each row. Also other mathematical operators (+, -, *, /) or logical operators (<, >, =,...) work element wise. The latter was already used in the subset data 008277 2000-01-07 22.579530 3.521204 0.829033 2000-01-08 4.577374 9.233151 0.466834 Boolean operators work as well: In [98]: df1 = pd.DataFrame({'a': [1, 0, 1], 'b': [0, 1, 1]}, dtype=bool) In [99]:

is made in one go and just using selection brackets [] is not sufficient anymore. The loc/iloc operators are required in front of the selection brackets []. When using loc/iloc, the part before the comma element-wise, so the / is applied for the values in each row. Also other mathematical operators (+, -, *, /) or logical operators (<, >, =,...) work element wise. The latter was already used in the subset data 008277 2000-01-07 22.579530 3.521204 0.829033 2000-01-08 4.577374 9.233151 0.466834 Boolean operators work as well: In [95]: df1 = pd.DataFrame({'a': [1, 0, 1], 'b': [0, 1, 1]}, dtype=bool) In [96]:

is made in one go and just using selection brackets [] is not sufficient anymore. The loc/iloc operators are required in front of the selection brackets []. When using loc/iloc, the 26 Chapter 1. Getting element-wise, so the / is applied for the values in each row. Also other mathematical operators (+, -, \*, /) or logical operators (<, >, =,...) work element wise. The latter was already used in the subset data 725974 6.437005e-01 0.420446 2.118275e+00 9 43.329821 4.196326e+00 3.227153 1.875802e+00 Boolean operators work as well: In [98]: df1 = pd.DataFrame({"a": [1, 0, 1], "b": [0, 1, 1]}, dtype=bool) In [99]:

is made in one go and just using selection brackets [] is not sufficient anymore. The loc/iloc operators are required in front of the selection brackets []. When using loc/iloc, the part before the comma element-wise, so the / is applied for the values in each row. Also other mathematical operators (+, -, \*, /) or logical operators (<, >, =,...) work element wise. The latter was already used in the subset data 725974 6.437005e-01 0.420446 2.118275e+00 9 43.329821 4.196326e+00 3.227153 1.875802e+00 Boolean operators work as well: In [98]: df1 = pd.DataFrame({"a": [1, 0, 1], "b": [0, 1, 1]}, dtype=bool) In [99]: