and usage of the above three libraries. 2.4. Dependencies 9 pandas: powerful Python data analysis toolkit, Release 0.25.3 {{ header }} 10 Chapter 2. Installation CHAPTER THREE GETTING STARTED {{ method for filtering: In [41]: df2 = df.copy() In [42]: df2['E'] = ['one', 'one', 'two', 'three', 'four', 'three'] In [43]: df2 Out[43]: A B C D E 2013-01-01 1.832747 1.515386 1.793547 -0.360634 one 2013-01-02 2013-01-04 0.509859 -2.769586 1.000521 -0.865748 three 2013-01-05 0.139488 -0.259328 1.082034 -0.902452 four 2013-01-06 -0.130327 -0.372906 1.072236 -0.424347 three In [44]: df2[df2['E'].isin(['two', 'four'])]

-------------------- Ran 818 tests in 21.631s OK (SKIP=2) 16 Chapter 2. Installation CHAPTER THREE FREQUENTLY ASKED QUESTIONS (FAQ) 17 pandas: powerful Python data analysis toolkit, Release 0.7 d NaN 4 b 2 2 a 1 1 In [257]: DataFrame(d, index=[’d’, ’b’, ’a’], columns=[’two’, ’three’]) Out[257]: two three d 4 NaN b 2 NaN a 1 NaN The row and column labels can be accessed respectively by from_items([(’A’, [1, 2, 3]), (’B’, [4, 5, 6])], .....: orient=’index’, columns=[’one’, ’two’, ’three’]) Out[275]: one two three A 1 2 3 B 4 5 6 30 Chapter 5. Intro to Data Structures pandas: powerful Python

-------------------- Ran 818 tests in 21.631s OK (SKIP=2) 16 Chapter 2. Installation CHAPTER THREE FREQUENTLY ASKED QUESTIONS (FAQ) 17 pandas: powerful Python data analysis toolkit, Release 0.7 d NaN 4 b 2 2 a 1 1 In [257]: DataFrame(d, index=[’d’, ’b’, ’a’], columns=[’two’, ’three’]) Out[257]: two three d 4 NaN b 2 NaN a 1 NaN The row and column labels can be accessed respectively by from_items([(’A’, [1, 2, 3]), (’B’, [4, 5, 6])], .....: orient=’index’, columns=[’one’, ’two’, ’three’]) Out[275]: one two three A 1 2 3 B 4 5 6 30 Chapter 5. Intro to Data Structures pandas: powerful Python

’bar’, .....: ’foo’, ’bar’, ’foo’, ’foo’], .....: ’B’ : [’one’, ’one’, ’two’, ’three’, .....: ’two’, ’two’, ’one’, ’three’], .....: ’C’ : np.random.randn(8), ’D’ : np.random.randn(8)}) In [929]: df Out[929]: 2 foo two -0.478428 0.254501 3 bar three 2.850221 -0.682682 4 foo two -0.350942 -0.697727 5 bar two -1.581790 -1.092094 6 foo one 1.113061 0.321042 7 foo three -1.868914 0.106481 In [930]: grouped -------------------- Ran 818 tests in 21.631s OK (SKIP=2) 20 Chapter 2. Installation CHAPTER THREE FREQUENTLY ASKED QUESTIONS (FAQ) 21 pandas: powerful Python data analysis toolkit, Release 0.7

as DatetimeIndex (GH7708) • pandas.tseries.holiday has added support for additional holidays and ways to observe holidays (GH7070) • pandas.tseries.holiday.Holiday now supports a list of offsets in Python3 What’s New pandas: powerful Python data analysis toolkit, Release 0.17.0 • Enhancing Performance, ways to enhance pandas performance with eval/query. Warning: In 0.13.0 Series has internally been refactored user-requested additions in order to support more explicit location based indexing. Pandas now supports three types of multi-axis indexing. • .loc is strictly label based, will raise KeyError when the items

• Bug in DataFrame.to_latex() with longtable=True where a latex multicolumn always spanned over three columns (GH17959) 1.1.5.4 Plotting • Bug in DataFrame.plot() and Series.plot() with DatetimeIndex end and period parameters were all specified, poten- tially leading to ambiguous ranges. When all three parameters were passed, interval_range ignored the period parameter, period_range ignored the end and avoid potentially ambiguous ranges, interval_range and period_range will now raise when all three parameters are passed. Previous Behavior: In [2]: pd.interval_range(start=0, end=4, periods=6) Out[2]:

analysis toolkit, Release 0.20.3 • pandas.tseries.holiday has added support for additional holidays and ways to observe holidays (GH7070) • pandas.tseries.holiday.Holiday now supports a list of offsets in Python3 learning pandas. • Comparison with R, idiom translations from R to pandas. • Enhancing Performance, ways to enhance pandas performance with eval/query. Warning: In 0.13.0 Series has internally been refactored user-requested additions in order to support more explicit location based indexing. Pandas now supports three types of multi-axis indexing. • .loc is strictly label based, will raise KeyError when the items

as DatetimeIndex (GH7708) • pandas.tseries.holiday has added support for additional holidays and ways to observe holidays (GH7070) • pandas.tseries.holiday.Holiday now supports a list of offsets in Python3 learning pandas. • Comparison with R, idiom translations from R to pandas. • Enhancing Performance, ways to enhance pandas performance with eval/query. Warning: In 0.13.0 Series has internally been refactored user-requested additions in order to support more explicit location based indexing. Pandas now supports three types of multi-axis indexing. • .loc is strictly label based, will raise KeyError when the items

learning pandas. • Comparison with R, idiom translations from R to pandas. • Enhancing Performance, ways to enhance pandas performance with eval/query. Warning: In 0.13.0 Series has internally been refactored user-requested additions in order to support more explicit location based indexing. Pandas now supports three types of multi-axis indexing. • .loc is strictly label based, will raise KeyError when the items tables. In [21]: index = MultiIndex(levels=[[’foo’, ’bar’, ’baz’, ’qux’], ....: [’one’, ’two’, ’three’]], ....: labels=[[0, 0, 0, 1, 1, 2, 2, 3, 3, 3], ....: [0, 1, 2, 0, 1, 1, 2, 0, 1, 2]], ....:

as DatetimeIndex (GH7708) • pandas.tseries.holiday has added support for additional holidays and ways to observe holidays (GH7070) • pandas.tseries.holiday.Holiday now supports a list of offsets in Python3 2014) 81 pandas: powerful Python data analysis toolkit, Release 0.15.2 • Enhancing Performance, ways to enhance pandas performance with eval/query. Warning: In 0.13.0 Series has internally been refactored user-requested additions in order to support more explicit location based indexing. Pandas now supports three types of multi-axis indexing. • .loc is strictly label based, will raise KeyError when the items