2,.15 1979,"D",.14,.05 1979,"E",.5,.15 1979,"F",1.2,.5 1979,"G",3.4,1.9 1979,"H",5.4,2.7 1979,"I",6.4,1.2 The index_col argument to read_csv can take a list of column numbers to turn multiple columns BlockIndex Block locations: array([0], dtype=int32) Block lengths: array([3], dtype=int32) {{ header }} 6.4 DataFrame 6.4.1 Constructor DataFrame([data, index, columns, dtype, copy]) Two-dimensional size-mutable 2d ndarray input See also: DataFrame.from_records Constructor from tuples, also record arrays. 6.4. DataFrame 1315 pandas: powerful Python data analysis toolkit, Release 0.25.0 DataFrame.from_dict

2,.15 1979,"D",.14,.05 1979,"E",.5,.15 1979,"F",1.2,.5 1979,"G",3.4,1.9 1979,"H",5.4,2.7 1979,"I",6.4,1.2 The index_col argument to read_csv can take a list of column numbers to turn multiple columns BlockIndex Block locations: array([0], dtype=int32) Block lengths: array([3], dtype=int32) {{ header }} 6.4 DataFrame 6.4.1 Constructor DataFrame([data, index, columns, dtype, copy]) Two-dimensional size-mutable 2d ndarray input See also: DataFrame.from_records Constructor from tuples, also record arrays. 6.4. DataFrame 1315 pandas: powerful Python data analysis toolkit, Release 0.25.1 DataFrame.from_dict

2,.15 1979,"D",.14,.05 1979,"E",.5,.15 1979,"F",1.2,.5 1979,"G",3.4,1.9 1979,"H",5.4,2.7 1979,"I",6.4,1.2 The index_col argument to read_csv can take a list of column numbers to turn multiple columns float64 BlockIndex Block locations: array([0], dtype=int32) Block lengths: array([3], dtype=int32) 6.4 DataFrame 6.4.1 Constructor DataFrame([data, index, columns, dtype, copy]) Two-dimensional size-mutable Can be thought of as a dict-like container for Series objects. The primary pandas data structure. 6.4. DataFrame 1331 pandas: powerful Python data analysis toolkit, Release 0.24.0 Parameters data [ndarray

binary operations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 45 6.4 Descriptive statistics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . x) In [34]: df1.combine(df2, combiner) Out[34]: A B 0 1 NaN 1 2 2 2 3 3 3 5 4 4 3 6 5 7 8 6.4 Descriptive statistics A large number of methods for computing descriptive statistics and other related also takes an optional level parameter which applies only if the object has a hierarchical index. 6.4. Descriptive statistics 49 pandas: powerful Python data analysis toolkit, Release 0.7.1 Function

binary operations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 45 6.4 Descriptive statistics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . x) In [34]: df1.combine(df2, combiner) Out[34]: A B 0 1 NaN 1 2 2 2 3 3 3 5 4 4 3 6 5 7 8 6.4 Descriptive statistics A large number of methods for computing descriptive statistics and other related also takes an optional level parameter which applies only if the object has a hierarchical index. 6.4. Descriptive statistics 49 pandas: powerful Python data analysis toolkit, Release 0.7.2 Function

binary operations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 51 6.4 Descriptive statistics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . x) In [34]: df1.combine(df2, combiner) Out[34]: A B 0 1 NaN 1 2 2 2 3 3 3 5 4 4 3 6 5 7 8 6.4 Descriptive statistics A large number of methods for computing descriptive statistics and other related also takes an optional level parameter which applies only if the object has a hierarchical index. 6.4. Descriptive statistics 55 pandas: powerful Python data analysis toolkit, Release 0.7.3 Function

2,.15 1979,"D",.14,.05 1979,"E",.5,.15 1979,"F",1.2,.5 1979,"G",3.4,1.9 1979,"H",5.4,2.7 1979,"I",6.4,1.2 The index_col argument to read_csv can take a list of column numbers to turn multiple columns in a DataFrame’s columns. >>> df = pd.DataFrame([[5.1, 3.5, 0], [4.9, 3.0, 0], [7.0, 3.2, 1], ... [6.4, 3.2, 1], [5.9, 3.0, 2]], ... columns=['length', 'width', 'species']) >>> ax1 = df.plot.scatter(x='length' topper-123 • vkk800 + • winlu + • ym-pett + • yrhooke + • ywpark1 + • zertrin • zhezherun + 6.4 Version 0.23 6.4.1 What’s new in 0.23.4 (August 3, 2018) {{ header }} This is a minor bug-fix release

What’s new in 0.24.0 (January 25, 2019) . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2464 6.4 Version 0.23 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2,.15 1979,"D",.14,.05 1979,"E",.5,.15 1979,"F",1.2,.5 1979,"G",3.4,1.9 1979,"H",5.4,2.7 1979,"I",6.4,1.2 The index_col argument to read_csv can take a list of column numbers to turn multiple columns in a DataFrame’s columns. >>> df = pd.DataFrame([[5.1, 3.5, 0], [4.9, 3.0, 0], [7.0, 3.2, 1], ... [6.4, 3.2, 1], [5.9, 3.0, 2]], ... columns=['length', 'width', 'species']) >>> ax1 = df.plot.scatter(x='length'

MultiIndexing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 101 6.4 Missing Data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . need for sortedness 6.3.3 Levels Prepending a level to a multiindex Flatten Hierarchical columns 6.4 Missing Data The missing data docs. 6.4.1 Replace Using replace with backrefs 6.5 Grouping The 2,.15 1979,"D",.14,.05 1979,"E",.5,.15 1979,"F",1.2,.5 1979,"G",3.4,1.9 1979,"H",5.4,2.7 1979,"I",6.4,1.2 The index_col argument to read_csv and read_table can take a list of column numbers to turn multiple

New Pandas Users . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 138 6.4 Excel charts with pandas, vincent and xlsxwriter . . . . . . . . . . . . . . . . . . . . . . . . . Converting between different kinds of formats • 11 - Lesson: - Combining data from various sources 6.4 Excel charts with pandas, vincent and xlsxwriter • Using Pandas and XlsxWriter to create Excel charts 2,.15 1979,"D",.14,.05 1979,"E",.5,.15 1979,"F",1.2,.5 1979,"G",3.4,1.9 1979,"H",5.4,2.7 1979,"I",6.4,1.2 The index_col argument to read_csv and read_table can take a list of column numbers to turn multiple