def main():
    # reindex
    obj = Series(range(4), index="a b c d".split(" ")[::-1])
    print obj

    obj2 = obj.reindex("a b c d e".split(" "))
    print obj2

    # Change NaN
    print obj.reindex("a b c d e".split(" "), fill_value=0)
    colors = ["blue", "purple", "yellow"]
    index = [0, 2, 4]
    obj3 = Series(colors, index=index)
    print obj3.reindex(range(6))
    print obj3.reindex(range(6), method="ffill")  # not found forward fill
    print obj3.reindex(range(6), method="backfill")  # bfill

    # DataFrame
    states = ["Ohio", "Texas", "California"]
    frame = DataFrame(np.arange(9).reshape((3, 3)), index="a b c".split(" "), columns=["Ohio", "Texas", "California"])
    print frame
    frame2 = frame.reindex("a b c d".split(" "))
    print frame2
    states[0] = "Utah"
    states[1], states[0] = states[:2]
    print frame.reindex(columns=states)
    # fill
    print frame.reindex("a b c d".split(" "), method="ffill", columns=states)
    print frame.ix["a b c d".split(" ")]
    print frame.ix["a b c d".split(" "), states]

    # Delete column
    print "", ""
    obj = Series(range(5), index="a b c d e".split(" "))
    new_obj = obj.drop("c")
    print new_obj
    print obj

    # Index reference
    print "", ""
    obj = Series(np.arange(4.0), index="a b c d".split(" "))
    print obj["b"]
    print obj[1]  # same
    print obj[2:4]
    print obj[["b", "a", "c"]]
    print obj[[1, 3]]
    print obj[obj < 2]
    # Slice with label
    print obj["b":"c"]  # include 'c'
    obj["b":"c"] = 5
    print obj

    data = DataFrame(
        np.arange(16).reshape((4, 4)),
        index=["Ohio", "Colorado", "Utah", "New York"],
        columns=["one", "two", "three", "four"],
    )
    print data
    # column
    print data["two"]
    print data[["three", "one"]]
    # row
    print data[:2]
    print data[data["three"] > 5]
    # all values
    print data < 5
    data[data < 5] = 0
    print data
    # row and column
    print data.ix[["Colorado"], ["two", "three"]]
    print data.ix[["Colorado", "Utah"], [3, 0, 1]]
    # row
    print data.ix[2]
    # label row and column, return column
    print data.ix[:"Utah", "two"]
    # xs
    # row
    print data.xs("Utah")
    print data.xs("Utah", axis=0)
    # rows
    print data.xs("two", axis=1)
    # icol/irow i is index
    print data.icol(1)
    print data.irow(1)

    # Union
    print "", ""
    s1 = Series([7.3, -2.5, 3.4, 1.5], index=["a", "c", "d", "e"])
    s2 = Series([-2.1, 3.6, -1.5, 4, 3.1], index=["a", "c", "e", "f", "g"])
    print s1
    print s2
    # index is union, but d, f, g are NaN
    print s1 + s2
    df1 = DataFrame(np.arange(9.0).reshape((3, 3)), columns=list("bcd"), index=["Ohio", "Texas", "Colorado"])
    df2 = DataFrame(np.arange(12.0).reshape((4, 3)), columns=list("bde"), index=["Utah", "Ohio", "Texas", "Oregon"])
    print df1
    print df2
    print df1 + df2

    # arithmetic method
    print "", ""
    df1 = DataFrame(np.arange(12.0).reshape((3, 4)), columns=list("abcd"))
    df2 = DataFrame(np.arange(20.0).reshape((4, 5)), columns=list("abcde"))
    print df1
    print df2
    print df1.add(df2, fill_value=0)
    # reindex has fill_value argument
    # other arithmetic method are sub/div/mul(ti)

    # Calculation in a DataFrame and Series
    print "", ""
    # subtract from each row. broadcat
    arr = np.arange(12.0).reshape((3, 4))
    print arr
    print arr[0]
    print arr - arr[0]
    frame = DataFrame(np.arange(12.0).reshape((4, 3)), columns=list("bde"), index=["Utah", "Ohio", "Texas", "Oregon"])
    series = frame.ix[0]
    print frame
    print series
    print frame - series

    series2 = Series(range(3), index=list("bef"))
    print frame + series2

    series3 = frame["d"]
    series4 = frame.ix[0]
    print frame
    print series3
    print series4
    print frame.sub(series3, axis=0)
    print frame.sub(series4, axis=1)

    # apply function and mapping
    print "", ""
    frame = DataFrame(np.arange(12.0).reshape((4, 3)), columns=list("bde"), index=["Utah", "Ohio", "Texas", "Oregon"])
    print frame
    f = lambda x: x.max() - x.min()
    print frame.apply(f)
    print frame.apply(f, axis=1)

    f = lambda x: Series([x.min(), x.max()], index=["min", "max"])
    print frame.apply(f)

    format = lambda x: "{0:.2f}".format(x)
    print frame.applymap(format)  # frame
    print frame["e"].map(format)  # series

    # sort and rank
    print "", ""
    obj = Series(range(4), index=list("dabc"))
    print obj
    print obj.sort_index()

    frame = DataFrame(np.arange(8).reshape((2, 4)), index=["three", "one"], columns=list("dabc"))
    print frame
    print frame.sort_index()
    print frame.sort_index(axis=1)
    print frame.sort_index(axis=1, ascending=False)

    # Sorting series
    print "", ""
    obj = Series([4, 7, -3, 2])
    print obj.order()
    obj = Series([4, np.nan, 7, np.nan, -3, 2])
    print obj.order()
    print obj.order(ascending=False)

    # order by multi columns
    print "", ""
    frame = DataFrame({"b": [4, 7, -3, 2], "a": [0, 1, 0, 1]})
    print frame.sort_index(by=["a", "b"])

    # rank
    print "", ""
    obj = Series([7, -5, 7, 4, 2, 0, 4])
    print obj.rank()  # method is average
    print obj.rank(method="first")  # No Duplicates
    print obj.rank(ascending=False, method="min")
    print obj.rank(ascending=False, method="max")
    f1 = DataFrame(obj, columns=["data"])
    f2 = DataFrame(obj.rank(), columns=["rank"])
    # merge by each index
    print pd.merge(f1, f2, left_index=True, right_index=True)

    # Index of the axis with duplicate values
    print "", ""
    obj = Series(range(5), index=list("aaabc"))
    print obj
    print obj.index.is_unique
    print obj["a"]
    print obj["c"]

    df = DataFrame(np.arange(12.0).reshape((4, 3)), index=list("aabb"), columns=list("ccd"))
    print df
    print df.ix["b"]
    print df["c"]