tests . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 387 3.5.3.2 Transitioning to pytest . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 388 3.5.3.3 Using pytest either a single character at each index of the original Series or NaN. For example, In [47]: strs = 'go', 'bow', 'joe', 'slow' In [48]: ds = Series(strs) In [49]: for s in ds.str: ....: print(s) ... all is not lost: you can inspect index.is_unique and raise an exception explicitly if it is False or go to a different code branch. 1.29 v.0.7.3 (April 12, 2012) This is a minor release from 0.7.2 and

tests . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 385 3.5.3.2 Transitioning to pytest . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 386 3.5.3.3 Using pytest either a single character at each index of the original Series or NaN. For example, In [47]: strs = 'go', 'bow', 'joe', 'slow' In [48]: ds = Series(strs) In [49]: for s in ds.str: ....: print(s) ... all is not lost: you can inspect index.is_unique and raise an exception explicitly if it is False or go to a different code branch. 1.28 v.0.7.3 (April 12, 2012) This is a minor release from 0.7.2 and

tests . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 415 3.5.3.2 Transitioning to pytest . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 415 3.5.3.3 Using pytest either a single character at each index of the original Series or NaN. For example, In [47]: strs = 'go', 'bow', 'joe', 'slow' In [48]: ds = Series(strs) In [49]: for s in ds.str: ....: print(s) ... all is not lost: you can inspect index.is_unique and raise an exception explicitly if it is False or go to a different code branch. 1.31 v.0.7.3 (April 12, 2012) This is a minor release from 0.7.2 and

: Assume we have two database tables of the same name and structure as our DataFrames. Now let’s go over the various types of JOINs. INNER JOIN SELECT * FROM df1 INNER JOIN df2 ON df1.key = df2.key; inconsistent dataset. If you rely on pandas to infer the dtypes of your columns, the parsing engine will go and infer the dtypes for different chunks of the data, rather than the whole dataset at once. Consequently dtype: object In [78]: s.iloc[8:10] Out[78]: Series([], dtype: object) Note that using slices that go out of bounds can result in an empty axis (e.g. an empty DataFrame being returned). In [79]: dfl =

: Assume we have two database tables of the same name and structure as our DataFrames. Now let’s go over the various types of JOINs. INNER JOIN SELECT * FROM df1 INNER JOIN df2 ON df1.key = df2.key; inconsistent dataset. If you rely on pandas to infer the dtypes of your columns, the parsing engine will go and infer the dtypes for different chunks of the data, rather than the whole dataset at once. Consequently \\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\Out[78]: ˓→Series([], dtype: object) Note that using slices that go out of bounds can result in an empty axis (e.g. an empty DataFrame being returned). In [79]: dfl =

: Assume we have two database tables of the same name and structure as our DataFrames. Now let’s go over the various types of JOINs. INNER JOIN SELECT * FROM df1 INNER JOIN df2 ON df1.key = df2.key; inconsistent dataset. If you rely on pandas to infer the dtypes of your columns, the parsing engine will go and infer the dtypes for different chunks of the data, rather than the whole dataset at once. Consequently \\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\Out[78]: ˓→Series([], dtype: object) Note that using slices that go out of bounds can result in an empty axis (e.g. an empty DataFrame being returned). In [79]: dfl =

Kingcome) Name: Name, dtype: object In this case, a subset of both rows and columns is made in one go and just using selection brackets [] is not sufficient anymore. The loc/iloc operators are required Torborg Danira female [16 rows x 3 columns] Again, a subset of both rows and columns is made in one go and just using selection brackets [] is not sufficient anymore. When specifically interested in certain : Assume we have two database tables of the same name and structure as our DataFrames. Now let’s go over the various types of JOINs. INNER JOIN SELECT * FROM df1 INNER JOIN df2 ON df1.key = df2.key;

Kingcome) Name: Name, dtype: object In this case, a subset of both rows and columns is made in one go and just using selection brackets [] is not sufficient anymore. The loc/iloc operators are required Torborg Danira female [16 rows x 3 columns] Again, a subset of both rows and columns is made in one go and just using selection brackets [] is not sufficient anymore. When specifically interested in certain : Assume we have two database tables of the same name and structure as our DataFrames. Now let’s go over the various types of JOINs. INNER JOIN SELECT * FROM df1 INNER JOIN df2 ON df1.key = df2.key;

Kingcome) Name: Name, dtype: object In this case, a subset of both rows and columns is made in one go and just using selection brackets [] is not sufficient anymore. The loc/iloc operators are required Torborg Danira female [16 rows x 3 columns] Again, a subset of both rows and columns is made in one go and just using selection brackets [] is not sufficient anymore. When specifically interested in certain : Assume we have two database tables of the same name and structure as our DataFrames. Now let’s go over the various types of JOINs. INNER JOIN SELECT * FROM df1 INNER JOIN df2 ON df1.key = df2.key;

Kingcome) Name: Name, dtype: object In this case, a subset of both rows and columns is made in one go and just using selection brackets [] is not sufficient anymore. The loc/iloc operators are required Torborg Danira female [16 rows x 3 columns] Again, a subset of both rows and columns is made in one go and just using selection brackets [] is not sufficient anymore. When specifically interested in certain : Assume we have two database tables of the same name and structure as our DataFrames. Now let’s go over the various types of JOINs. INNER JOIN SELECT * FROM df1 INNER JOIN df2 ON df1.key = df2.key;