diff --git a/scripts/bench_join.R b/scripts/bench_join.R
deleted file mode 100644
index edba277f0295c..0000000000000
--- a/scripts/bench_join.R
+++ /dev/null
@@ -1,50 +0,0 @@
-library(xts)
-
-iterations <- 50
-
-ns = c(100, 1000, 10000, 100000, 1000000)
-kinds = c("outer", "left", "inner")
-
-result = matrix(0, nrow=3, ncol=length(ns))
-n <- 100000
-pct.overlap <- 0.2
-
-k <- 1
-
-for (ni in 1:length(ns)){
- n <- ns[ni]
- rng1 <- 1:n
- offset <- as.integer(n * pct.overlap)
- rng2 <- rng1 + offset
- x <- xts(matrix(rnorm(n * k), nrow=n, ncol=k),
-          as.POSIXct(Sys.Date()) + rng1)
- y <- xts(matrix(rnorm(n * k), nrow=n, ncol=k),
-          as.POSIXct(Sys.Date()) + rng2)
- timing <- numeric()
- for (i in 1:3) {
-     kind = kinds[i]
-     for(j in 1:iterations) {
-       gc()  # just to be sure
-       timing[j] <- system.time(merge(x,y,join=kind))[3]
-     }
-     #timing <- system.time(for (j in 1:iterations) merge.xts(x, y, join=kind),
-     #                      gcFirst=F)
-     #timing <- as.list(timing)
-     result[i, ni] <- mean(timing) * 1000
-     #result[i, ni] = (timing$elapsed / iterations) * 1000
-   }
-}
-
-rownames(result) <- kinds
-colnames(result) <- log10(ns)
-
-mat <- matrix(rnorm(500000), nrow=100000, ncol=5)
-set.seed(12345)
-indexer <- sample(1:100000)
-
-timing <- rep(0, 10)
-for (i in 1:10) {
-  gc()
-  timing[i] = system.time(mat[indexer,])[3]
-}
-
diff --git a/scripts/bench_join.py b/scripts/bench_join.py
deleted file mode 100644
index f9d43772766d8..0000000000000
--- a/scripts/bench_join.py
+++ /dev/null
@@ -1,211 +0,0 @@
-from pandas.compat import range, lrange
-import numpy as np
-import pandas._libs.lib as lib
-from pandas import *
-from copy import deepcopy
-import time
-
-n = 1000000
-K = 1
-pct_overlap = 0.2
-
-a = np.arange(n, dtype=np.int64)
-b = np.arange(n * pct_overlap, n * (1 + pct_overlap), dtype=np.int64)
-
-dr1 = DatetimeIndex('1/1/2000', periods=n, offset=offsets.Minute())
-dr2 = DatetimeIndex(
-    dr1[int(pct_overlap * n)], periods=n, offset=offsets.Minute(2))
-
-aobj = a.astype(object)
-bobj = b.astype(object)
-
-av = np.random.randn(n)
-bv = np.random.randn(n)
-
-avf = np.random.randn(n, K)
-bvf = np.random.randn(n, K)
-
-a_series = Series(av, index=a)
-b_series = Series(bv, index=b)
-
-a_frame = DataFrame(avf, index=a, columns=lrange(K))
-b_frame = DataFrame(bvf, index=b, columns=lrange(K, 2 * K))
-
-
-def do_left_join(a, b, av, bv):
-    out = np.empty((len(a), 2))
-    lib.left_join_1d(a, b, av, bv, out)
-    return out
-
-
-def do_outer_join(a, b, av, bv):
-    result_index, aindexer, bindexer = lib.outer_join_indexer(a, b)
-    result = np.empty((2, len(result_index)))
-    lib.take_1d(av, aindexer, result[0])
-    lib.take_1d(bv, bindexer, result[1])
-    return result_index, result
-
-
-def do_inner_join(a, b, av, bv):
-    result_index, aindexer, bindexer = lib.inner_join_indexer(a, b)
-    result = np.empty((2, len(result_index)))
-    lib.take_1d(av, aindexer, result[0])
-    lib.take_1d(bv, bindexer, result[1])
-    return result_index, result
-
-from line_profiler import LineProfiler
-prof = LineProfiler()
-
-from pandas.util.testing import set_trace
-
-
-def do_left_join_python(a, b, av, bv):
-    indexer, mask = lib.ordered_left_join_int64(a, b)
-
-    n, ak = av.shape
-    _, bk = bv.shape
-    result_width = ak + bk
-
-    result = np.empty((result_width, n), dtype=np.float64)
-    result[:ak] = av.T
-
-    bchunk = result[ak:]
-    _take_multi(bv.T, indexer, bchunk)
-    np.putmask(bchunk, np.tile(mask, bk), np.nan)
-    return result
-
-
-def _take_multi(data, indexer, out):
-    if not data.flags.c_contiguous:
-        data = data.copy()
-    for i in range(data.shape[0]):
-        data[i].take(indexer, out=out[i])
-
-
-def do_left_join_multi(a, b, av, bv):
-    n, ak = av.shape
-    _, bk = bv.shape
-    result = np.empty((n, ak + bk), dtype=np.float64)
-    lib.left_join_2d(a, b, av, bv, result)
-    return result
-
-
-def do_outer_join_multi(a, b, av, bv):
-    n, ak = av.shape
-    _, bk = bv.shape
-    result_index, rindexer, lindexer = lib.outer_join_indexer(a, b)
-    result = np.empty((len(result_index), ak + bk), dtype=np.float64)
-    lib.take_join_contiguous(av, bv, lindexer, rindexer, result)
-    # result = np.empty((ak + bk, len(result_index)), dtype=np.float64)
-    # lib.take_axis0(av, rindexer, out=result[:ak].T)
-    # lib.take_axis0(bv, lindexer, out=result[ak:].T)
-    return result_index, result
-
-
-def do_inner_join_multi(a, b, av, bv):
-    n, ak = av.shape
-    _, bk = bv.shape
-    result_index, rindexer, lindexer = lib.inner_join_indexer(a, b)
-    result = np.empty((len(result_index), ak + bk), dtype=np.float64)
-    lib.take_join_contiguous(av, bv, lindexer, rindexer, result)
-    # result = np.empty((ak + bk, len(result_index)), dtype=np.float64)
-    # lib.take_axis0(av, rindexer, out=result[:ak].T)
-    # lib.take_axis0(bv, lindexer, out=result[ak:].T)
-    return result_index, result
-
-
-def do_left_join_multi_v2(a, b, av, bv):
-    indexer, mask = lib.ordered_left_join_int64(a, b)
-    bv_taken = bv.take(indexer, axis=0)
-    np.putmask(bv_taken, mask.repeat(bv.shape[1]), np.nan)
-    return np.concatenate((av, bv_taken), axis=1)
-
-
-def do_left_join_series(a, b):
-    return b.reindex(a.index)
-
-
-def do_left_join_frame(a, b):
-    a.index._indexMap = None
-    b.index._indexMap = None
-    return a.join(b, how='left')
-
-
-# a = np.array([1, 2, 3, 4, 5], dtype=np.int64)
-# b = np.array([0, 3, 5, 7, 9], dtype=np.int64)
-# print(lib.inner_join_indexer(a, b))
-
-out = np.empty((10, 120000))
-
-
-def join(a, b, av, bv, how="left"):
-    func_dict = {'left': do_left_join_multi,
-                 'outer': do_outer_join_multi,
-                 'inner': do_inner_join_multi}
-
-    f = func_dict[how]
-    return f(a, b, av, bv)
-
-
-def bench_python(n=100000, pct_overlap=0.20, K=1):
-    import gc
-    ns = [2, 3, 4, 5, 6]
-    iterations = 200
-    pct_overlap = 0.2
-    kinds = ['outer', 'left', 'inner']
-
-    all_results = {}
-    for logn in ns:
-        n = 10 ** logn
-        a = np.arange(n, dtype=np.int64)
-        b = np.arange(n * pct_overlap, n * pct_overlap + n, dtype=np.int64)
-
-        avf = np.random.randn(n, K)
-        bvf = np.random.randn(n, K)
-
-        a_frame = DataFrame(avf, index=a, columns=lrange(K))
-        b_frame = DataFrame(bvf, index=b, columns=lrange(K, 2 * K))
-
-        all_results[logn] = result = {}
-
-        for kind in kinds:
-            gc.disable()
-            elapsed = 0
-            _s = time.clock()
-            for i in range(iterations):
-                if i % 10 == 0:
-                    elapsed += time.clock() - _s
-                    gc.collect()
-                    _s = time.clock()
-                a_frame.join(b_frame, how=kind)
-                # join(a, b, avf, bvf, how=kind)
-            elapsed += time.clock() - _s
-            gc.enable()
-            result[kind] = (elapsed / iterations) * 1000
-
-    return DataFrame(all_results, index=kinds)
-
-
-def bench_xts(n=100000, pct_overlap=0.20):
-    from pandas.rpy.common import r
-    r('a <- 5')
-
-    xrng = '1:%d' % n
-
-    start = n * pct_overlap + 1
-    end = n + start - 1
-    yrng = '%d:%d' % (start, end)
-
-    r('library(xts)')
-
-    iterations = 500
-
-    kinds = ['left', 'outer', 'inner']
-    result = {}
-    for kind in kinds:
-        r('x <- xts(rnorm(%d), as.POSIXct(Sys.Date()) + %s)' % (n, xrng))
-        r('y <- xts(rnorm(%d), as.POSIXct(Sys.Date()) + %s)' % (n, yrng))
-        stmt = 'for (i in 1:%d) merge(x, y, join="%s")' % (iterations, kind)
-        elapsed = r('as.list(system.time(%s, gcFirst=F))$elapsed' % stmt)[0]
-        result[kind] = (elapsed / iterations) * 1000
-    return Series(result)
diff --git a/scripts/bench_join_multi.py b/scripts/bench_join_multi.py
deleted file mode 100644
index b19da6a2c47d8..0000000000000
--- a/scripts/bench_join_multi.py
+++ /dev/null
@@ -1,32 +0,0 @@
-from pandas import *
-
-import numpy as np
-from pandas.compat import zip, range, lzip
-from pandas.util.testing import rands
-import pandas._libs.lib as lib
-
-N = 100000
-
-key1 = [rands(10) for _ in range(N)]
-key2 = [rands(10) for _ in range(N)]
-
-zipped = lzip(key1, key2)
-
-
-def _zip(*args):
-    arr = np.empty(N, dtype=object)
-    arr[:] = lzip(*args)
-    return arr
-
-
-def _zip2(*args):
-    return lib.list_to_object_array(lzip(*args))
-
-index = MultiIndex.from_arrays([key1, key2])
-to_join = DataFrame({'j1': np.random.randn(100000)}, index=index)
-
-data = DataFrame({'A': np.random.randn(500000),
-                  'key1': np.repeat(key1, 5),
-                  'key2': np.repeat(key2, 5)})
-
-# data.join(to_join, on=['key1', 'key2'])
diff --git a/scripts/bench_refactor.py b/scripts/bench_refactor.py
deleted file mode 100644
index dafba371e995a..0000000000000
--- a/scripts/bench_refactor.py
+++ /dev/null
@@ -1,51 +0,0 @@
-from pandas import *
-from pandas.compat import range
-try:
-    import pandas.core.internals as internals
-    reload(internals)
-    import pandas.core.frame as frame
-    reload(frame)
-    from pandas.core.frame import DataFrame as DataMatrix
-except ImportError:
-    pass
-
-N = 1000
-K = 500
-
-
-def horribly_unconsolidated():
-    index = np.arange(N)
-
-    df = DataMatrix(index=index)
-
-    for i in range(K):
-        df[i] = float(K)
-
-    return df
-
-
-def bench_reindex_index(df, it=100):
-    new_idx = np.arange(0, N, 2)
-    for i in range(it):
-        df.reindex(new_idx)
-
-
-def bench_reindex_columns(df, it=100):
-    new_cols = np.arange(0, K, 2)
-    for i in range(it):
-        df.reindex(columns=new_cols)
-
-
-def bench_join_index(df, it=10):
-    left = df.reindex(index=np.arange(0, N, 2),
-                      columns=np.arange(K // 2))
-    right = df.reindex(columns=np.arange(K // 2 + 1, K))
-    for i in range(it):
-        joined = left.join(right)
-
-if __name__ == '__main__':
-    df = horribly_unconsolidated()
-    left = df.reindex(index=np.arange(0, N, 2),
-                      columns=np.arange(K // 2))
-    right = df.reindex(columns=np.arange(K // 2 + 1, K))
-    bench_join_index(df)
diff --git a/scripts/boxplot_test.py b/scripts/boxplot_test.py
deleted file mode 100644
index 3704f7b60dc60..0000000000000
--- a/scripts/boxplot_test.py
+++ /dev/null
@@ -1,14 +0,0 @@
-import matplotlib.pyplot as plt
-
-import random
-import pandas.util.testing as tm
-tm.N = 1000
-df = tm.makeTimeDataFrame()
-import string
-foo = list(string.letters[:5]) * 200
-df['indic'] = list(string.letters[:5]) * 200
-random.shuffle(foo)
-df['indic2'] = foo
-df.boxplot(by=['indic', 'indic2'], fontsize=8, rot=90)
-
-plt.show()
diff --git a/scripts/count_code.sh b/scripts/count_code.sh
deleted file mode 100755
index 991faf2e8711b..0000000000000
--- a/scripts/count_code.sh
+++ /dev/null
@@ -1 +0,0 @@
-cloc pandas --force-lang=Python,pyx --not-match-f="parser.c|lib.c|tslib.c|sandbox.c|hashtable.c|sparse.c|algos.c|index.c"
\ No newline at end of file
diff --git a/scripts/faster_xs.py b/scripts/faster_xs.py
deleted file mode 100644
index 2bb6271124c4f..0000000000000
--- a/scripts/faster_xs.py
+++ /dev/null
@@ -1,15 +0,0 @@
-import numpy as np
-
-import pandas.util.testing as tm
-
-from pandas.core.internals import _interleaved_dtype
-
-df = tm.makeDataFrame()
-
-df['E'] = 'foo'
-df['F'] = 'foo'
-df['G'] = 2
-df['H'] = df['A'] > 0
-
-blocks = df._data.blocks
-items = df.columns
diff --git a/scripts/file_sizes.py b/scripts/file_sizes.py
deleted file mode 100644
index de03c72ffbd09..0000000000000
--- a/scripts/file_sizes.py
+++ /dev/null
@@ -1,208 +0,0 @@
-from __future__ import print_function
-import os
-import sys
-
-import numpy as np
-import matplotlib.pyplot as plt
-
-from pandas import DataFrame
-from pandas.util.testing import set_trace
-from pandas import compat
-
-dirs = []
-names = []
-lengths = []
-
-if len(sys.argv) > 1:
-    loc = sys.argv[1]
-else:
-    loc = '.'
-walked = os.walk(loc)
-
-
-def _should_count_file(path):
-    return path.endswith('.py') or path.endswith('.pyx')
-
-
-def _is_def_line(line):
-    """def/cdef/cpdef, but not `cdef class`"""
-    return (line.endswith(':') and not 'class' in line.split() and
-            (line.startswith('def ') or
-             line.startswith('cdef ') or
-             line.startswith('cpdef ') or
-             ' def ' in line or ' cdef ' in line or ' cpdef ' in line))
-
-
-class LengthCounter(object):
-    """
-    should add option for subtracting nested function lengths??
-    """
-    def __init__(self, lines):
-        self.lines = lines
-        self.pos = 0
-        self.counts = []
-        self.n = len(lines)
-
-    def get_counts(self):
-        self.pos = 0
-        self.counts = []
-        while self.pos < self.n:
-            line = self.lines[self.pos]
-            self.pos += 1
-            if _is_def_line(line):
-                level = _get_indent_level(line)
-                self._count_function(indent_level=level)
-        return self.counts
-
-    def _count_function(self, indent_level=1):
-        indent = '    ' * indent_level
-
-        def _end_of_function(line):
-            return (line != '' and
-                    not line.startswith(indent) and
-                    not line.startswith('#'))
-
-        start_pos = self.pos
-        while self.pos < self.n:
-            line = self.lines[self.pos]
-            if _end_of_function(line):
-                self._push_count(start_pos)
-                return
-
-            self.pos += 1
-
-            if _is_def_line(line):
-                self._count_function(indent_level=indent_level + 1)
-
-        # end of file
-        self._push_count(start_pos)
-
-    def _push_count(self, start_pos):
-        func_lines = self.lines[start_pos:self.pos]
-
-        if len(func_lines) > 300:
-            set_trace()
-
-        # remove blank lines at end
-        while len(func_lines) > 0 and func_lines[-1] == '':
-            func_lines = func_lines[:-1]
-
-        # remove docstrings and comments
-        clean_lines = []
-        in_docstring = False
-        for line in func_lines:
-            line = line.strip()
-            if in_docstring and _is_triplequote(line):
-                in_docstring = False
-                continue
-
-            if line.startswith('#'):
-                continue
-
-            if _is_triplequote(line):
-                in_docstring = True
-                continue
-
-        self.counts.append(len(func_lines))
-
-
-def _get_indent_level(line):
-    level = 0
-    while line.startswith('    ' * level):
-        level += 1
-    return level
-
-
-def _is_triplequote(line):
-    return line.startswith('"""') or line.startswith("'''")
-
-
-def _get_file_function_lengths(path):
-    lines = [x.rstrip() for x in open(path).readlines()]
-    counter = LengthCounter(lines)
-    return counter.get_counts()
-
-# def test_get_function_lengths():
-text = """
-class Foo:
-
-def foo():
-    def bar():
-        a = 1
-
-        b = 2
-
-        c = 3
-
-    foo = 'bar'
-
-def x():
-    a = 1
-
-    b = 3
-
-    c = 7
-
-    pass
-"""
-
-expected = [5, 8, 7]
-
-lines = [x.rstrip() for x in text.splitlines()]
-counter = LengthCounter(lines)
-result = counter.get_counts()
-assert(result == expected)
-
-
-def doit():
-    for directory, _, files in walked:
-        print(directory)
-        for path in files:
-            if not _should_count_file(path):
-                continue
-
-            full_path = os.path.join(directory, path)
-            print(full_path)
-            lines = len(open(full_path).readlines())
-
-            dirs.append(directory)
-            names.append(path)
-            lengths.append(lines)
-
-    result = DataFrame({'dirs': dirs, 'names': names,
-                        'lengths': lengths})
-
-
-def doit2():
-    counts = {}
-    for directory, _, files in walked:
-        print(directory)
-        for path in files:
-            if not _should_count_file(path) or path.startswith('test_'):
-                continue
-
-            full_path = os.path.join(directory, path)
-            counts[full_path] = _get_file_function_lengths(full_path)
-
-    return counts
-
-counts = doit2()
-
-# counts = _get_file_function_lengths('pandas/tests/test_series.py')
-
-all_counts = []
-for k, v in compat.iteritems(counts):
-    all_counts.extend(v)
-all_counts = np.array(all_counts)
-
-fig = plt.figure(figsize=(10, 5))
-ax = fig.add_subplot(111)
-ax.hist(all_counts, bins=100)
-n = len(all_counts)
-nmore = (all_counts > 50).sum()
-ax.set_title('%s function lengths, n=%d' % ('pandas', n))
-ax.set_ylabel('N functions')
-ax.set_xlabel('Function length')
-ax.text(100, 300, '%.3f%% with > 50 lines' % ((n - nmore) / float(n)),
-        fontsize=18)
-plt.show()
diff --git a/scripts/gen_release_notes.py b/scripts/gen_release_notes.py
deleted file mode 100644
index 7e4ffca59a0ab..0000000000000
--- a/scripts/gen_release_notes.py
+++ /dev/null
@@ -1,95 +0,0 @@
-from __future__ import print_function
-import sys
-import json
-from pandas.io.common import urlopen
-from datetime import datetime
-
-
-class Milestone(object):
-
-    def __init__(self, title, number):
-        self.title = title
-        self.number = number
-
-    def __eq__(self, other):
-        if isinstance(other, Milestone):
-            return self.number == other.number
-        return False
-
-
-class Issue(object):
-
-    def __init__(self, title, labels, number, milestone, body, state):
-        self.title = title
-        self.labels = set([x['name'] for x in labels])
-        self.number = number
-        self.milestone = milestone
-        self.body = body
-        self.closed = state == 'closed'
-
-    def __eq__(self, other):
-        if isinstance(other, Issue):
-            return self.number == other.number
-        return False
-
-
-def get_issues():
-    all_issues = []
-    page_number = 1
-    while True:
-        iss = _get_page(page_number)
-        if len(iss) == 0:
-            break
-        page_number += 1
-        all_issues.extend(iss)
-    return all_issues
-
-
-def _get_page(page_number):
-    gh_url = ('https://api.github.com/repos/pandas-dev/pandas/issues?'
-              'milestone=*&state=closed&assignee=*&page=%d') % page_number
-    with urlopen(gh_url) as resp:
-        rs = resp.readlines()[0]
-    jsondata = json.loads(rs)
-    issues = [Issue(x['title'], x['labels'], x['number'],
-                    get_milestone(x['milestone']), x['body'], x['state'])
-              for x in jsondata]
-    return issues
-
-
-def get_milestone(data):
-    if data is None:
-        return None
-    return Milestone(data['title'], data['number'])
-
-
-def collate_label(issues, label):
-    lines = []
-    for x in issues:
-        if label in x.labels:
-            lines.append('\t- %s(#%d)' % (x.title, x.number))
-
-    return '\n'.join(lines)
-
-
-def release_notes(milestone):
-    issues = get_issues()
-
-    headers = ['New Features', 'Improvements to existing features',
-               'API Changes', 'Bug fixes']
-    labels = ['New', 'Enhancement', 'API-Change', 'Bug']
-
-    rs = 'pandas %s' % milestone
-    rs += '\n' + ('=' * len(rs))
-    rs += '\n\n **Release date:** %s' % datetime.today().strftime('%B %d, %Y')
-    for i, h in enumerate(headers):
-        rs += '\n\n**%s**\n\n' % h
-        l = labels[i]
-        rs += collate_label(issues, l)
-
-    return rs
-
-if __name__ == '__main__':
-
-    rs = release_notes(sys.argv[1])
-    print(rs)
diff --git a/scripts/git-mrb b/scripts/git-mrb
deleted file mode 100644
index c15e6dbf9f51a..0000000000000
--- a/scripts/git-mrb
+++ /dev/null
@@ -1,82 +0,0 @@
-#!/usr/bin/env python
-"""git-mrb: merge remote branch.
-
-git mrb [remote:branch OR remote-branch] [onto] [upstream]
-
-remote must be locally available, and branch must exist in that remote.
-
-If 'onto' branch isn't given, default is 'master'.
-
-If 'upstream' repository isn't given, default is 'origin'.
-
-You can separate the remote and branch spec with either a : or a -.
-
-Taken from IPython project
-"""
-#-----------------------------------------------------------------------------
-# Imports
-#-----------------------------------------------------------------------------
-
-from subprocess import check_call
-import sys
-
-#-----------------------------------------------------------------------------
-# Functions
-#-----------------------------------------------------------------------------
-
-def sh(cmd):
-    cmd = cmd.format(**shvars)
-    print('$', cmd)
-    check_call(cmd, shell=True)
-
-#-----------------------------------------------------------------------------
-# Main Script
-#-----------------------------------------------------------------------------
-
-argv = sys.argv[1:]
-narg = len(argv)
-
-try:
-    branch_spec = argv[0]
-    sep = ':' if ':' in branch_spec else '-'
-    remote, branch = branch_spec.split(':', 1)
-    if not branch:
-        raise ValueError('Branch spec %s invalid, branch not found' %
-                         branch_spec)
-except:
-    import traceback as tb
-    tb.print_exc()
-    print(__doc__)
-    sys.exit(1)
-
-onto = argv[1] if narg >= 2 else 'master'
-upstream = argv[1] if narg == 3 else 'origin'
-
-# Git doesn't like ':' in branch names.
-if sep == ':':
-    branch_spec = branch_spec.replace(':', '-')
-
-# Global used by sh
-shvars = dict(remote=remote, branch_spec=branch_spec, branch=branch,
-              onto=onto, upstream=upstream)
-
-# Start git calls.
-sh('git fetch {remote}')
-sh('git checkout -b {branch_spec} {onto}')
-sh('git merge {remote}/{branch}')
-
-print("""
-*************************************************************
- Run test suite.  If tests pass, run the following to merge:
-
-git checkout {onto}
-git merge {branch_spec}
-git push {upstream} {onto}
-
-*************************************************************
-""".format(**shvars))
-
-ans = raw_input("Revert to master and delete temporary branch? [Y/n]: ")
-if ans.strip().lower() in ('', 'y', 'yes'):
-    sh('git checkout {onto}')
-    sh('git branch -D {branch_spec}')
\ No newline at end of file
diff --git a/scripts/git_code_churn.py b/scripts/git_code_churn.py
deleted file mode 100644
index 18c9b244a6ba0..0000000000000
--- a/scripts/git_code_churn.py
+++ /dev/null
@@ -1,34 +0,0 @@
-import subprocess
-import os
-import re
-import sys
-
-import numpy as np
-
-from pandas import *
-
-
-if __name__ == '__main__':
-    from vbench.git import GitRepo
-    repo = GitRepo('/Users/wesm/code/pandas')
-    churn = repo.get_churn_by_file()
-
-    file_include = []
-    for path in churn.major_axis:
-        if path.endswith('.pyx') or path.endswith('.py'):
-            file_include.append(path)
-    commits_include = [sha for sha in churn.minor_axis
-                       if 'LF' not in repo.messages[sha]]
-    commits_include.remove('dcf3490')
-
-    clean_churn = churn.reindex(major=file_include, minor=commits_include)
-
-    by_commit = clean_churn.sum('major').sum(1)
-
-    by_date = by_commit.groupby(repo.commit_date).sum()
-
-    by_date = by_date.drop([datetime(2011, 6, 10)])
-
-    # clean out days where I touched Cython
-
-    by_date = by_date[by_date < 5000]
diff --git a/scripts/groupby_sample.py b/scripts/groupby_sample.py
deleted file mode 100644
index 42008858d3cad..0000000000000
--- a/scripts/groupby_sample.py
+++ /dev/null
@@ -1,54 +0,0 @@
-from pandas import *
-import numpy as np
-import string
-import pandas.compat as compat
-
-g1 = np.array(list(string.letters))[:-1]
-g2 = np.arange(510)
-df_small = DataFrame({'group1': ["a", "b", "a", "a", "b", "c", "c", "c", "c",
-                                 "c", "a", "a", "a", "b", "b", "b", "b"],
-                      'group2': [1, 2, 3, 4, 1, 3, 5, 6, 5, 4, 1, 2, 3, 4, 3, 2, 1],
-                      'value': ["apple", "pear", "orange", "apple",
-                                "banana", "durian", "lemon", "lime",
-                                "raspberry", "durian", "peach", "nectarine",
-                                "banana", "lemon", "guava", "blackberry",
-                                "grape"]})
-value = df_small['value'].values.repeat(3)
-df = DataFrame({'group1': g1.repeat(4000 * 5),
-                'group2': np.tile(g2, 400 * 5),
-                'value': value.repeat(4000 * 5)})
-
-
-def random_sample():
-    grouped = df.groupby(['group1', 'group2'])['value']
-    from random import choice
-    choose = lambda group: choice(group.index)
-    indices = grouped.apply(choose)
-    return df.reindex(indices)
-
-
-def random_sample_v2():
-    grouped = df.groupby(['group1', 'group2'])['value']
-    from random import choice
-    choose = lambda group: choice(group.index)
-    indices = [choice(v) for k, v in compat.iteritems(grouped.groups)]
-    return df.reindex(indices)
-
-
-def do_shuffle(arr):
-    from random import shuffle
-    result = arr.copy().values
-    shuffle(result)
-    return result
-
-
-def shuffle_uri(df, grouped):
-    perm = np.r_[tuple([np.random.permutation(
-        idxs) for idxs in compat.itervalues(grouped.groups)])]
-    df['state_permuted'] = np.asarray(df.ix[perm]['value'])
-
-df2 = df.copy()
-grouped = df2.groupby('group1')
-shuffle_uri(df2, grouped)
-
-df2['state_perm'] = grouped['value'].transform(do_shuffle)
diff --git a/scripts/groupby_speed.py b/scripts/groupby_speed.py
deleted file mode 100644
index 3be9fac12418e..0000000000000
--- a/scripts/groupby_speed.py
+++ /dev/null
@@ -1,35 +0,0 @@
-from __future__ import print_function
-from pandas import *
-
-rng = DatetimeIndex('1/3/2011', '11/30/2011', offset=offsets.Minute())
-
-df = DataFrame(np.random.randn(len(rng), 5), index=rng,
-               columns=list('OHLCV'))
-
-rng5 = DatetimeIndex('1/3/2011', '11/30/2011', offset=offsets.Minute(5))
-gp = rng5.asof
-grouped = df.groupby(gp)
-
-
-def get1(dt):
-    k = gp(dt)
-    return grouped.get_group(k)
-
-
-def get2(dt):
-    k = gp(dt)
-    return df.ix[grouped.groups[k]]
-
-
-def f():
-    for i, date in enumerate(df.index):
-        if i % 10000 == 0:
-            print(i)
-        get1(date)
-
-
-def g():
-    for i, date in enumerate(df.index):
-        if i % 10000 == 0:
-            print(i)
-        get2(date)
diff --git a/scripts/groupby_test.py b/scripts/groupby_test.py
deleted file mode 100644
index f640a6ed79503..0000000000000
--- a/scripts/groupby_test.py
+++ /dev/null
@@ -1,145 +0,0 @@
-from collections import defaultdict
-
-from numpy import nan
-import numpy as np
-
-from pandas import *
-
-import pandas._libs.lib as tseries
-import pandas.core.groupby as gp
-import pandas.util.testing as tm
-from pandas.compat import range
-reload(gp)
-
-"""
-
-k = 1000
-values = np.random.randn(8 * k)
-key1 = np.array(['foo', 'bar', 'baz', 'bar', 'foo', 'baz', 'bar', 'baz'] * k,
-                dtype=object)
-key2 = np.array(['b', 'b', 'b', 'b', 'a', 'a', 'a', 'a' ] * k,
-                dtype=object)
-shape, labels, idicts = gp.labelize(key1, key2)
-
-print(tseries.group_labels(key1))
-
-# print(shape)
-# print(labels)
-# print(idicts)
-
-result = tseries.group_aggregate(values, labels, shape)
-
-print(tseries.groupby_indices(key2))
-
-df = DataFrame({'key1' : key1,
-                'key2' : key2,
-                'v1' : values,
-                'v2' : values})
-k1 = df['key1']
-k2 = df['key2']
-
-# del df['key1']
-# del df['key2']
-
-# r2 = gp.multi_groupby(df, np.sum, k1, k2)
-
-# print(result)
-
-gen = gp.generate_groups(df['v1'], labels, shape, axis=1,
-                         factory=DataFrame)
-
-res = defaultdict(dict)
-for a, gen1 in gen:
-    for b, group in gen1:
-        print(a, b)
-        print(group)
-        # res[b][a] = group['values'].sum()
-        res[b][a] = group.sum()
-
-res = DataFrame(res)
-
-grouped = df.groupby(['key1', 'key2'])
-"""
-
-# data = {'A' : [0, 0, 0, 0, 1, 1, 1, 1, 1, 1., nan, nan],
-#         'B' : ['A', 'B'] * 6,
-#         'C' : np.random.randn(12)}
-# df = DataFrame(data)
-# df['C'][2:10:2] = nan
-
-# single column
-# grouped = df.drop(['B'], axis=1).groupby('A')
-# exp = {}
-# for cat, group in grouped:
-#     exp[cat] = group['C'].sum()
-# exp = DataFrame({'C' : exp})
-# result = grouped.sum()
-
-# grouped = df.groupby(['A', 'B'])
-# expd = {}
-# for cat1, cat2, group in grouped:
-#     expd.setdefault(cat1, {})[cat2] = group['C'].sum()
-# exp = DataFrame(expd).T.stack()
-# result = grouped.sum()['C']
-
-# print('wanted')
-# print(exp)
-# print('got')
-# print(result)
-
-# tm.N = 10000
-
-# mapping = {'A': 0, 'C': 1, 'B': 0, 'D': 1}
-# tf = lambda x: x - x.mean()
-
-# df = tm.makeTimeDataFrame()
-# ts = df['A']
-
-# # grouped = df.groupby(lambda x: x.strftime('%m/%y'))
-# grouped = df.groupby(mapping, axis=1)
-# groupedT = df.T.groupby(mapping, axis=0)
-
-# r1 = groupedT.transform(tf).T
-# r2 = grouped.transform(tf)
-
-# fillit = lambda x: x.fillna(method='pad')
-
-# f = lambda x: x
-
-# transformed = df.groupby(lambda x: x.strftime('%m/%y')).transform(lambda
-# x: x)
-
-# def ohlc(group):
-#     return Series([group[0], group.max(), group.min(), group[-1]],
-#                   index=['open', 'high', 'low', 'close'])
-# grouper = [lambda x: x.year, lambda x: x.month]
-# dr = DateRange('1/1/2000', '1/1/2002')
-# ts = Series(np.random.randn(len(dr)), index=dr)
-
-# import string
-
-# k = 20
-# n = 1000
-
-# keys = list(string.letters[:k])
-
-# df = DataFrame({'A' : np.tile(keys, n),
-#                 'B' : np.repeat(keys[:k/2], n * 2),
-#                 'C' : np.random.randn(k * n)})
-
-# def f():
-#     for x in df.groupby(['A', 'B']):
-#         pass
-
-a = np.arange(100).repeat(100)
-b = np.tile(np.arange(100), 100)
-index = MultiIndex.from_arrays([a, b])
-s = Series(np.random.randn(len(index)), index)
-df = DataFrame({'A': s})
-df['B'] = df.index.get_level_values(0)
-df['C'] = df.index.get_level_values(1)
-
-
-def f():
-    for x in df.groupby(['B', 'B']):
-        pass
diff --git a/scripts/hdfstore_panel_perf.py b/scripts/hdfstore_panel_perf.py
deleted file mode 100644
index c66e9506fc4c5..0000000000000
--- a/scripts/hdfstore_panel_perf.py
+++ /dev/null
@@ -1,17 +0,0 @@
-from pandas import *
-from pandas.util.testing import rands
-from pandas.compat import range
-
-i, j, k = 7, 771, 5532
-
-panel = Panel(np.random.randn(i, j, k),
-              items=[rands(10) for _ in range(i)],
-              major_axis=DatetimeIndex('1/1/2000', periods=j,
-                                       offset=offsets.Minute()),
-              minor_axis=[rands(10) for _ in range(k)])
-
-
-store = HDFStore('test.h5')
-store.put('test_panel', panel, table=True)
-
-retrieved = store['test_panel']
diff --git a/scripts/json_manip.py b/scripts/json_manip.py
deleted file mode 100644
index 7ff4547825568..0000000000000
--- a/scripts/json_manip.py
+++ /dev/null
@@ -1,423 +0,0 @@
-"""
-
-Tasks
--------
-
-Search and transform jsonable structures, specifically to make it 'easy' to make tabular/csv output for other consumers.
-
-Example
-~~~~~~~~~~~~~
-
-    *give me a list of all the fields called 'id' in this stupid, gnarly
-    thing*
-
-    >>> Q('id',gnarly_data)
-    ['id1','id2','id3']
-
-
-Observations:
----------------------
-
-1) 'simple data structures' exist and are common.  They are tedious
-   to search.
-
-2)  The DOM is another nested / treeish structure, and jQuery selector is
-    a good tool for that.
-
-3a) R, Numpy, Excel and other analysis tools want 'tabular' data.  These
-    analyses are valuable and worth doing.
-
-3b) Dot/Graphviz, NetworkX, and some other analyses *like* treeish/dicty
-    things, and those analyses are also worth doing!
-
-3c) Some analyses are best done using 'one-off' and custom code in C, Python,
-    or another 'real' programming language.
-
-4)  Arbitrary transforms are tedious and error prone.  SQL is one solution,
-    XSLT is another,
-
-5)  the XPATH/XML/XSLT family is.... not universally loved :)  They are
-    very complete, and the completeness can make simple cases... gross.
-
-6)  For really complicated data structures, we can write one-off code.  Getting
-    80% of the way is mostly okay.  There will always have to be programmers
-    in the loop.
-
-7)  Re-inventing SQL is probably a failure mode.  So is reinventing XPATH, XSLT
-    and the like.  Be wary of mission creep!  Re-use when possible (e.g., can
-    we put the thing into a DOM using
-
-8)  If the interface is good, people can improve performance later.
-
-
-Simplifying
----------------
-
-
-1)  Assuming 'jsonable' structures
-
-2)  keys are strings or stringlike.  Python allows any hashable to be a key.
-    for now, we pretend that doesn't happen.
-
-3)  assumes most dicts are 'well behaved'.  DAG, no cycles!
-
-4)  assume that if people want really specialized transforms, they can do it
-    themselves.
-
-"""
-from __future__ import print_function
-
-from collections import namedtuple
-import csv
-import itertools
-from itertools import product
-from operator import attrgetter as aget, itemgetter as iget
-import operator
-import sys
-from pandas.compat import map, u, callable, Counter
-import pandas.compat as compat
-
-
-##  note 'url' appears multiple places and not all extensions have same struct
-ex1 = {
-    'name': 'Gregg',
-    'extensions': [
-        {'id':'hello',
-         'url':'url1'},
-        {'id':'gbye',
-         'url':'url2',
-         'more': dict(url='url3')},
-    ]
-}
-
-## much longer example
-ex2 = {u('metadata'): {u('accessibilities'): [{u('name'): u('accessibility.tabfocus'),
-    u('value'): 7},
-   {u('name'): u('accessibility.mouse_focuses_formcontrol'), u('value'): False},
-   {u('name'): u('accessibility.browsewithcaret'), u('value'): False},
-   {u('name'): u('accessibility.win32.force_disabled'), u('value'): False},
-   {u('name'): u('accessibility.typeaheadfind.startlinksonly'), u('value'): False},
-   {u('name'): u('accessibility.usebrailledisplay'), u('value'): u('')},
-   {u('name'): u('accessibility.typeaheadfind.timeout'), u('value'): 5000},
-   {u('name'): u('accessibility.typeaheadfind.enabletimeout'), u('value'): True},
-   {u('name'): u('accessibility.tabfocus_applies_to_xul'), u('value'): False},
-   {u('name'): u('accessibility.typeaheadfind.flashBar'), u('value'): 1},
-   {u('name'): u('accessibility.typeaheadfind.autostart'), u('value'): True},
-   {u('name'): u('accessibility.blockautorefresh'), u('value'): False},
-   {u('name'): u('accessibility.browsewithcaret_shortcut.enabled'),
-    u('value'): True},
-   {u('name'): u('accessibility.typeaheadfind.enablesound'), u('value'): True},
-   {u('name'): u('accessibility.typeaheadfind.prefillwithselection'),
-    u('value'): True},
-   {u('name'): u('accessibility.typeaheadfind.soundURL'), u('value'): u('beep')},
-   {u('name'): u('accessibility.typeaheadfind'), u('value'): False},
-   {u('name'): u('accessibility.typeaheadfind.casesensitive'), u('value'): 0},
-   {u('name'): u('accessibility.warn_on_browsewithcaret'), u('value'): True},
-   {u('name'): u('accessibility.usetexttospeech'), u('value'): u('')},
-   {u('name'): u('accessibility.accesskeycausesactivation'), u('value'): True},
-   {u('name'): u('accessibility.typeaheadfind.linksonly'), u('value'): False},
-   {u('name'): u('isInstantiated'), u('value'): True}],
-  u('extensions'): [{u('id'): u('216ee7f7f4a5b8175374cd62150664efe2433a31'),
-    u('isEnabled'): True},
-   {u('id'): u('1aa53d3b720800c43c4ced5740a6e82bb0b3813e'), u('isEnabled'): False},
-   {u('id'): u('01ecfac5a7bd8c9e27b7c5499e71c2d285084b37'), u('isEnabled'): True},
-   {u('id'): u('1c01f5b22371b70b312ace94785f7b0b87c3dfb2'), u('isEnabled'): True},
-   {u('id'): u('fb723781a2385055f7d024788b75e959ad8ea8c3'), u('isEnabled'): True}],
-  u('fxVersion'): u('9.0'),
-  u('location'): u('zh-CN'),
-  u('operatingSystem'): u('WINNT Windows NT 5.1'),
-  u('surveyAnswers'): u(''),
-  u('task_guid'): u('d69fbd15-2517-45b5-8a17-bb7354122a75'),
-  u('tpVersion'): u('1.2'),
-  u('updateChannel'): u('beta')},
- u('survey_data'): {
-  u('extensions'): [{u('appDisabled'): False,
-    u('id'): u('testpilot?labs.mozilla.com'),
-    u('isCompatible'): True,
-    u('isEnabled'): True,
-    u('isPlatformCompatible'): True,
-    u('name'): u('Test Pilot')},
-   {u('appDisabled'): True,
-    u('id'): u('dict?www.youdao.com'),
-    u('isCompatible'): False,
-    u('isEnabled'): False,
-    u('isPlatformCompatible'): True,
-    u('name'): u('Youdao Word Capturer')},
-   {u('appDisabled'): False,
-    u('id'): u('jqs?sun.com'),
-    u('isCompatible'): True,
-    u('isEnabled'): True,
-    u('isPlatformCompatible'): True,
-    u('name'): u('Java Quick Starter')},
-   {u('appDisabled'): False,
-    u('id'): u('?20a82645-c095-46ed-80e3-08825760534b?'),
-    u('isCompatible'): True,
-    u('isEnabled'): True,
-    u('isPlatformCompatible'): True,
-    u('name'): u('Microsoft .NET Framework Assistant')},
-   {u('appDisabled'): False,
-    u('id'): u('?a0d7ccb3-214d-498b-b4aa-0e8fda9a7bf7?'),
-    u('isCompatible'): True,
-    u('isEnabled'): True,
-    u('isPlatformCompatible'): True,
-    u('name'): u('WOT')}],
-  u('version_number'): 1}}
-
-# class SurveyResult(object):
-
-#     def __init__(self, record):
-#         self.record = record
-#         self.metadata, self.survey_data = self._flatten_results()
-
-#     def _flatten_results(self):
-#         survey_data = self.record['survey_data']
-#         extensions = DataFrame(survey_data['extensions'])
-
-def denorm(queries,iterable_of_things,default=None):
-    """
-    'repeat', or 'stutter' to 'tableize' for downstream.
-    (I have no idea what a good word for this is!)
-
-    Think ``kronecker`` products, or:
-
-    ``SELECT single,multiple FROM table;``
-
-    single   multiple
-    -------  ---------
-    id1      val1
-    id1      val2
-
-
-    Args:
-
-        queries:  iterable of ``Q`` queries.
-        iterable_of_things:  to be queried.
-
-    Returns:
-
-        list of 'stuttered' output, where if a query returns
-        a 'single', it gets repeated appropriately.
-
-
-    """
-
-    def _denorm(queries,thing):
-        fields = []
-        results = []
-        for q in queries:
-            #print(q)
-            r = Ql(q,thing)
-            #print("-- result: ", r)
-            if not r:
-                r = [default]
-            if isinstance(r[0], type({})):
-                fields.append(sorted(r[0].keys()))  # dicty answers
-            else:
-                fields.append([q])  # stringy answer
-
-            results.append(r)
-
-        #print(results)
-        #print(fields)
-        flist =  list(flatten(*map(iter,fields)))
-
-        prod = itertools.product(*results)
-        for p in prod:
-            U = dict()
-            for (ii,thing) in enumerate(p):
-                #print(ii,thing)
-                if isinstance(thing, type({})):
-                    U.update(thing)
-                else:
-                    U[fields[ii][0]] = thing
-
-            yield U
-
-    return list(flatten(*[_denorm(queries,thing) for thing in iterable_of_things]))
-
-
-def default_iget(fields,default=None,):
-    """ itemgetter with 'default' handling, that *always* returns lists
-
-    API CHANGES from ``operator.itemgetter``
-
-    Note: Sorry to break the iget api... (fields vs *fields)
-    Note: *always* returns a list... unlike itemgetter,
-        which can return tuples or 'singles'
-    """
-    myiget = operator.itemgetter(*fields)
-    L = len(fields)
-    def f(thing):
-        try:
-            ans = list(myiget(thing))
-            if L < 2:
-                ans = [ans,]
-            return ans
-        except KeyError:
-            # slower!
-            return [thing.get(x,default) for x in fields]
-
-    f.__doc__ = "itemgetter with default %r for fields %r" %(default,fields)
-    f.__name__ = "default_itemgetter"
-    return f
-
-
-def flatten(*stack):
-    """
-    helper function for flattening iterables of generators in a
-    sensible way.
-    """
-    stack = list(stack)
-    while stack:
-        try: x = next(stack[0])
-        except StopIteration:
-            stack.pop(0)
-            continue
-        if hasattr(x,'next') and callable(getattr(x,'next')):
-            stack.insert(0, x)
-
-        #if isinstance(x, (GeneratorType,listerator)):
-        else: yield x
-
-
-def _Q(filter_, thing):
-    """ underlying machinery for Q function recursion """
-    T = type(thing)
-    if isinstance({}, T):
-        for k,v in compat.iteritems(thing):
-            #print(k,v)
-            if filter_ == k:
-                if isinstance(v, type([])):
-                    yield iter(v)
-                else:
-                    yield v
-
-            if type(v)  in (type({}),type([])):
-                yield Q(filter_,v)
-
-    elif isinstance([], T):
-        for k in thing:
-            #print(k)
-            yield Q(filter_,k)
-
-    else:
-        # no recursion.
-        pass
-
-def Q(filter_,thing):
-    """
-    type(filter):
-    - list:  a flattened list of all searches (one list)
-    - dict:  dict with vals each of which is that search
-
-    Notes:
-
-    [1] 'parent thing', with space, will do a descendent
-    [2] this will come back 'flattened' jQuery style
-    [3] returns a generator.  Use ``Ql`` if you want a list.
-
-    """
-    if isinstance(filter_, type([])):
-        return flatten(*[_Q(x,thing) for x in filter_])
-    elif isinstance(filter_, type({})):
-        d = dict.fromkeys(list(filter_.keys()))
-        #print(d)
-        for k in d:
-            #print(flatten(Q(k,thing)))
-            d[k] = Q(k,thing)
-
-        return d
-
-    else:
-        if " " in filter_:   # i.e. "antecendent post"
-            parts = filter_.strip().split()
-            r = None
-            for p in parts:
-                r = Ql(p,thing)
-                thing = r
-
-            return r
-
-        else:  # simple.
-            return flatten(_Q(filter_,thing))
-
-def Ql(filter_,thing):
-    """ same as Q, but returns a list, not a generator """
-    res = Q(filter_,thing)
-
-    if isinstance(filter_, type({})):
-        for k in res:
-            res[k] = list(res[k])
-        return res
-
-    else:
-        return list(res)
-
-
-
-def countit(fields,iter_of_iter,default=None):
-    """
-    note: robust to fields not being in i_of_i, using ``default``
-    """
-    C = Counter()  # needs hashables
-    T = namedtuple("Thing",fields)
-    get = default_iget(*fields,default=default)
-    return Counter(
-        (T(*get(thing)) for thing in iter_of_iter)
-    )
-
-
-## right now this works for one row...
-def printout(queries,things,default=None, f=sys.stdout, **kwargs):
-    """ will print header and objects
-
-    **kwargs go to csv.DictWriter
-
-    help(csv.DictWriter) for more.
-    """
-
-    results = denorm(queries,things,default=None)
-    fields = set(itertools.chain(*(x.keys() for x in results)))
-
-    W = csv.DictWriter(f=f,fieldnames=fields,**kwargs)
-    #print("---prod---")
-    #print(list(prod))
-    W.writeheader()
-    for r in results:
-        W.writerow(r)
-
-
-def test_run():
-    print("\n>>> print(list(Q('url',ex1)))")
-    print(list(Q('url',ex1)))
-    assert  list(Q('url',ex1)) == ['url1','url2','url3']
-    assert Ql('url',ex1) == ['url1','url2','url3']
-
-    print("\n>>>  print(list(Q(['name','id'],ex1)))")
-    print(list(Q(['name','id'],ex1)))
-    assert Ql(['name','id'],ex1) == ['Gregg','hello','gbye']
-
-
-    print("\n>>> print(Ql('more url',ex1))")
-    print(Ql('more url',ex1))
-
-
-    print("\n>>> list(Q('extensions',ex1))")
-    print(list(Q('extensions',ex1)))
-
-    print("\n>>> print(Ql('extensions',ex1))")
-    print(Ql('extensions',ex1))
-
-    print("\n>>> printout(['name','extensions'],[ex1,], extrasaction='ignore')")
-    printout(['name','extensions'],[ex1,], extrasaction='ignore')
-
-    print("\n\n")
-
-    from pprint import pprint as pp
-
-    print("-- note that the extension fields are also flattened!  (and N/A) -- ")
-    pp(denorm(['location','fxVersion','notthere','survey_data extensions'],[ex2,], default="N/A")[:2])
-
-
-if __name__ == "__main__":
-    pass
diff --git a/scripts/leak.py b/scripts/leak.py
deleted file mode 100644
index 47f74bf020597..0000000000000
--- a/scripts/leak.py
+++ /dev/null
@@ -1,13 +0,0 @@
-from pandas import *
-from pandas.compat import range
-import numpy as np
-import pandas.util.testing as tm
-import os
-import psutil
-
-pid = os.getpid()
-proc = psutil.Process(pid)
-
-df = DataFrame(index=np.arange(100))
-for i in range(5000):
-    df[i] = 5
diff --git a/scripts/parser_magic.py b/scripts/parser_magic.py
deleted file mode 100644
index 72fef39d8db65..0000000000000
--- a/scripts/parser_magic.py
+++ /dev/null
@@ -1,74 +0,0 @@
-from pandas.util.testing import set_trace
-import pandas.util.testing as tm
-import pandas.compat as compat
-
-from pandas import *
-import ast
-import inspect
-import sys
-
-
-def merge(a, b):
-    f, args, _ = parse_stmt(inspect.currentframe().f_back)
-    return DataFrame({args[0]: a,
-                      args[1]: b})
-
-
-def parse_stmt(frame):
-    info = inspect.getframeinfo(frame)
-    call = info[-2][0]
-    mod = ast.parse(call)
-    body = mod.body[0]
-    if isinstance(body, (ast.Assign, ast.Expr)):
-        call = body.value
-    elif isinstance(body, ast.Call):
-        call = body
-    return _parse_call(call)
-
-
-def _parse_call(call):
-    func = _maybe_format_attribute(call.func)
-
-    str_args = []
-    for arg in call.args:
-        if isinstance(arg, ast.Name):
-            str_args.append(arg.id)
-        elif isinstance(arg, ast.Call):
-            formatted = _format_call(arg)
-            str_args.append(formatted)
-
-    return func, str_args, {}
-
-
-def _format_call(call):
-    func, args, kwds = _parse_call(call)
-    content = ''
-    if args:
-        content += ', '.join(args)
-    if kwds:
-        fmt_kwds = ['%s=%s' % item for item in compat.iteritems(kwds)]
-        joined_kwds = ', '.join(fmt_kwds)
-        if args:
-            content = content + ', ' + joined_kwds
-        else:
-            content += joined_kwds
-    return '%s(%s)' % (func, content)
-
-
-def _maybe_format_attribute(name):
-    if isinstance(name, ast.Attribute):
-        return _format_attribute(name)
-    return name.id
-
-
-def _format_attribute(attr):
-    obj = attr.value
-    if isinstance(attr.value, ast.Attribute):
-        obj = _format_attribute(attr.value)
-    else:
-        obj = obj.id
-    return '.'.join((obj, attr.attr))
-
-a = tm.makeTimeSeries()
-b = tm.makeTimeSeries()
-df = merge(a, b)
diff --git a/scripts/preepoch_test.py b/scripts/preepoch_test.py
deleted file mode 100644
index 36a3d768e671f..0000000000000
--- a/scripts/preepoch_test.py
+++ /dev/null
@@ -1,23 +0,0 @@
-import numpy as np
-from pandas import *
-
-
-def panda_test():
-
-    # generate some data
-    data = np.random.rand(50, 5)
-    # generate some dates
-    dates = DatetimeIndex('1/1/1969', periods=50)
-    # generate column headings
-    cols = ['A', 'B', 'C', 'D', 'E']
-
-    df = DataFrame(data, index=dates, columns=cols)
-
-    # save to HDF5Store
-    store = HDFStore('bugzilla.h5', mode='w')
-    store['df'] = df  # This gives: OverflowError: mktime argument out of range
-    store.close()
-
-
-if __name__ == '__main__':
-    panda_test()
diff --git a/scripts/pypistats.py b/scripts/pypistats.py
deleted file mode 100644
index 41343f6d30c76..0000000000000
--- a/scripts/pypistats.py
+++ /dev/null
@@ -1,101 +0,0 @@
-#!/usr/bin/env python
-# -*- coding: utf-8 -*-
-
-"""
-Calculates the total number of downloads that a particular PyPI package has
-received across all versions tracked by PyPI
-"""
-
-from datetime import datetime
-import locale
-import sys
-import xmlrpclib
-import pandas as pd
-
-locale.setlocale(locale.LC_ALL, '')
-
-
-class PyPIDownloadAggregator(object):
-
-    def __init__(self, package_name, include_hidden=True):
-        self.package_name = package_name
-        self.include_hidden = include_hidden
-        self.proxy = xmlrpclib.Server('http://pypi.python.org/pypi')
-        self._downloads = {}
-
-    @property
-    def releases(self):
-        """Retrieves the release number for each uploaded release"""
-
-        result = self.proxy.package_releases(self.package_name,
-                                             self.include_hidden)
-
-        if len(result) == 0:
-            # no matching package--search for possibles, and limit to 15
-            # results
-            results = self.proxy.search({
-                'name': self.package_name,
-                'description': self.package_name
-            }, 'or')[:15]
-
-            # make sure we only get unique package names
-            matches = []
-            for match in results:
-                name = match['name']
-                if name not in matches:
-                    matches.append(name)
-
-            # if only one package was found, return it
-            if len(matches) == 1:
-                self.package_name = matches[0]
-                return self.releases
-
-            error = """No such package found: %s
-
-Possible matches include:
-%s
-""" % (self.package_name, '\n'.join('\t- %s' % n for n in matches))
-
-            sys.exit(error)
-
-        return result
-
-    def get_downloads(self):
-        """Calculate the total number of downloads for the package"""
-        downloads = {}
-        for release in self.releases:
-            urls = self.proxy.release_urls(self.package_name, release)
-            urls = pd.DataFrame(urls)
-            urls['version'] = release
-            downloads[release] = urls
-
-        return pd.concat(downloads, ignore_index=True)
-
-if __name__ == '__main__':
-    agg = PyPIDownloadAggregator('pandas')
-
-    data = agg.get_downloads()
-
-    to_omit = ['0.2b1', '0.2beta']
-
-    isostrings = data['upload_time'].map(lambda x: x.value)
-    data['upload_time'] = pd.to_datetime(isostrings)
-
-    totals = data.groupby('version').downloads.sum()
-    rollup = {'0.8.0rc1': '0.8.0',
-              '0.8.0rc2': '0.8.0',
-              '0.3.0.beta': '0.3.0',
-              '0.3.0.beta2': '0.3.0'}
-    downloads = totals.groupby(lambda x: rollup.get(x, x)).sum()
-
-    first_upload = data.groupby('version').upload_time.min()
-
-    result = pd.DataFrame({'downloads': totals,
-                           'release_date': first_upload})
-    result = result.sort('release_date')
-    result = result.drop(to_omit + list(rollup.keys()))
-    result.index.name = 'release'
-
-    by_date = result.reset_index().set_index('release_date').downloads
-    dummy = pd.Series(index=pd.DatetimeIndex([datetime(2012, 12, 27)]))
-    by_date = by_date.append(dummy).shift(1).fillna(0)
diff --git a/scripts/roll_median_leak.py b/scripts/roll_median_leak.py
deleted file mode 100644
index 03f39e2b18372..0000000000000
--- a/scripts/roll_median_leak.py
+++ /dev/null
@@ -1,26 +0,0 @@
-from __future__ import print_function
-from pandas import *
-
-import numpy as np
-import os
-
-from vbench.api import Benchmark
-from pandas.util.testing import rands
-from pandas.compat import range
-import pandas._libs.lib as lib
-import pandas._sandbox as sbx
-import time
-
-import psutil
-
-pid = os.getpid()
-proc = psutil.Process(pid)
-
-lst = SparseList()
-lst.append([5] * 10000)
-lst.append(np.repeat(np.nan, 1000000))
-
-for _ in range(10000):
-    print(proc.get_memory_info())
-    sdf = SparseDataFrame({'A': lst.to_array()})
-    chunk = sdf[sdf['A'] == 5]
diff --git a/scripts/runtests.py b/scripts/runtests.py
deleted file mode 100644
index e14752b43116b..0000000000000
--- a/scripts/runtests.py
+++ /dev/null
@@ -1,5 +0,0 @@
-from __future__ import print_function
-import os
-print(os.getpid())
-import nose
-nose.main('pandas.core')
diff --git a/scripts/test_py27.bat b/scripts/test_py27.bat
deleted file mode 100644
index 11e3056287e31..0000000000000
--- a/scripts/test_py27.bat
+++ /dev/null
@@ -1,6 +0,0 @@
-SET PATH=C:\MinGW\bin;C:\Python27;C:\Python27\Scripts;%PATH%
-
-python setup.py clean
-python setup.py build_ext -c mingw32 --inplace
-
-nosetests pandas
\ No newline at end of file
diff --git a/scripts/testmed.py b/scripts/testmed.py
deleted file mode 100644
index dd3b952d58c60..0000000000000
--- a/scripts/testmed.py
+++ /dev/null
@@ -1,171 +0,0 @@
-## {{{ Recipe 576930 (r10): Efficient Running Median using an Indexable Skiplist
-
-from random import random
-from math import log, ceil
-from pandas.compat import range
-from numpy.random import randn
-from pandas.lib.skiplist import rolling_median
-
-
-class Node(object):
-    __slots__ = 'value', 'next', 'width'
-
-    def __init__(self, value, next, width):
-        self.value, self.next, self.width = value, next, width
-
-
-class End(object):
-    'Sentinel object that always compares greater than another object'
-    def __cmp__(self, other):
-        return 1
-
-NIL = Node(End(), [], [])               # Singleton terminator node
-
-
-class IndexableSkiplist:
-    'Sorted collection supporting O(lg n) insertion, removal, and lookup by rank.'
-
-    def __init__(self, expected_size=100):
-        self.size = 0
-        self.maxlevels = int(1 + log(expected_size, 2))
-        self.head = Node('HEAD', [NIL] * self.maxlevels, [1] * self.maxlevels)
-
-    def __len__(self):
-        return self.size
-
-    def __getitem__(self, i):
-        node = self.head
-        i += 1
-        for level in reversed(range(self.maxlevels)):
-            while node.width[level] <= i:
-                i -= node.width[level]
-                node = node.next[level]
-        return node.value
-
-    def insert(self, value):
-        # find first node on each level where node.next[levels].value > value
-        chain = [None] * self.maxlevels
-        steps_at_level = [0] * self.maxlevels
-        node = self.head
-        for level in reversed(range(self.maxlevels)):
-            while node.next[level].value <= value:
-                steps_at_level[level] += node.width[level]
-                node = node.next[level]
-            chain[level] = node
-
-        # insert a link to the newnode at each level
-        d = min(self.maxlevels, 1 - int(log(random(), 2.0)))
-        newnode = Node(value, [None] * d, [None] * d)
-        steps = 0
-        for level in range(d):
-            prevnode = chain[level]
-            newnode.next[level] = prevnode.next[level]
-            prevnode.next[level] = newnode
-            newnode.width[level] = prevnode.width[level] - steps
-            prevnode.width[level] = steps + 1
-            steps += steps_at_level[level]
-        for level in range(d, self.maxlevels):
-            chain[level].width[level] += 1
-        self.size += 1
-
-    def remove(self, value):
-        # find first node on each level where node.next[levels].value >= value
-        chain = [None] * self.maxlevels
-        node = self.head
-        for level in reversed(range(self.maxlevels)):
-            while node.next[level].value < value:
-                node = node.next[level]
-            chain[level] = node
-        if value != chain[0].next[0].value:
-            raise KeyError('Not Found')
-
-        # remove one link at each level
-        d = len(chain[0].next[0].next)
-        for level in range(d):
-            prevnode = chain[level]
-            prevnode.width[level] += prevnode.next[level].width[level] - 1
-            prevnode.next[level] = prevnode.next[level].next[level]
-        for level in range(d, self.maxlevels):
-            chain[level].width[level] -= 1
-        self.size -= 1
-
-    def __iter__(self):
-        'Iterate over values in sorted order'
-        node = self.head.next[0]
-        while node is not NIL:
-            yield node.value
-            node = node.next[0]
-
-from collections import deque
-from itertools import islice
-
-
-class RunningMedian:
-    'Fast running median with O(lg n) updates where n is the window size'
-
-    def __init__(self, n, iterable):
-        from pandas.lib.skiplist import IndexableSkiplist as skiplist
-
-        self.it = iter(iterable)
-        self.queue = deque(islice(self.it, n))
-        self.skiplist = IndexableSkiplist(n)
-        for elem in self.queue:
-            self.skiplist.insert(elem)
-
-    def __iter__(self):
-        queue = self.queue
-        skiplist = self.skiplist
-        midpoint = len(queue) // 2
-        yield skiplist[midpoint]
-        for newelem in self.it:
-            oldelem = queue.popleft()
-            skiplist.remove(oldelem)
-            queue.append(newelem)
-            skiplist.insert(newelem)
-            yield skiplist[midpoint]
-
-N = 100000
-K = 10000
-
-import time
-
-
-def test():
-    from numpy.random import randn
-
-    arr = randn(N)
-
-    def _test(arr, k):
-        meds = RunningMedian(k, arr)
-        return list(meds)
-
-    _test(arr, K)
-
-
-
-def test2():
-
-    arr = randn(N)
-
-    return rolling_median(arr, K)
-
-
-def runmany(f, arr, arglist):
-    timings = []
-
-    for arg in arglist:
-        tot = 0
-        for i in range(5):
-            tot += _time(f, arr, arg)
-        timings.append(tot / 5)
-
-    return timings
-
-
-def _time(f, *args):
-    _start = time.clock()
-    result = f(*args)
-    return time.clock() - _start
-
-if __name__ == '__main__':
-    test2()
diff --git a/scripts/touchup_gh_issues.py b/scripts/touchup_gh_issues.py
deleted file mode 100755
index 8aa6d426156f0..0000000000000
--- a/scripts/touchup_gh_issues.py
+++ /dev/null
@@ -1,44 +0,0 @@
-#!/usr/bin/env python
-# -*- coding: utf-8 -*-
-
-from __future__ import print_function
-from collections import OrderedDict
-import sys
-import re
-
-"""
-Reads in stdin, replace all occurences of '#num' or 'GH #num' with
-links to github issue. dumps the issue anchors before the next
-section header
-"""
-
-pat = "((?:\s*GH\s*)?)#(\d{3,4})([^_]|$)?"
-rep_pat = r"\1GH\2_\3"
-anchor_pat = ".. _GH{id}: https://github.com/pandas-dev/pandas/issues/{id}"
-section_pat = "^pandas\s[\d\.]+\s*$"
-
-
-def main():
-    issues = OrderedDict()
-    while True:
-
-        line = sys.stdin.readline()
-        if not line:
-            break
-
-        if re.search(section_pat, line):
-            for id in issues:
-                print(anchor_pat.format(id=id).rstrip())
-            if issues:
-                print("\n")
-            issues = OrderedDict()
-
-        for m in re.finditer(pat, line):
-            id = m.group(2)
-            if id not in issues:
-                issues[id] = True
-        print(re.sub(pat, rep_pat, line).rstrip())
-    pass
-
-if __name__ == "__main__":
-    main()
diff --git a/scripts/use_build_cache.py b/scripts/use_build_cache.py
deleted file mode 100755
index f8c2df2a8a45d..0000000000000
--- a/scripts/use_build_cache.py
+++ /dev/null
@@ -1,354 +0,0 @@
-#!/usr/bin/env python
-# -*- coding: utf-8 -*-
-import os
-
-"""
-This script should be run from the repo root dir, it rewrites setup.py
-to use the build cache directory specified in the envar BUILD_CACHE_DIR
-or in a file named .build_cache_dir in the repo root directory.
-
-Artifacts included in the cache:
-- gcc artifacts
-- The .c files resulting from cythonizing pyx/d files
-- 2to3 refactoring results (when run under python3)
-
-Tested on releases back to 0.7.0.
-
-"""
-
-try:
-    import argparse
-    argparser = argparse.ArgumentParser(description="""
-    'Program description.
-    """.strip())
-
-    argparser.add_argument('-f', '--force-overwrite',
-                    default=False,
-                   help='Setting this will overwrite any existing cache results for the current commit',
-                   action='store_true')
-    argparser.add_argument('-d', '--debug',
-                    default=False,
-                   help='Report cache hits/misses',
-                   action='store_true')
-
-    args = argparser.parse_args()
-except:
-    class Foo(object):
-        debug=False
-        force_overwrite=False
-
-    args = Foo() # for 2.6, no argparse
-
-#print(args.accumulate(args.integers))
-
-shim="""
-import os
-import sys
-import shutil
-import warnings
-import re
-"""
-
-shim += ("BC_FORCE_OVERWRITE = %s\n" % args.force_overwrite)
-shim += ("BC_DEBUG = %s\n" % args.debug)
-
-shim += """
-try:
-    if not ("develop" in sys.argv) and not ("install" in sys.argv):
-        1/0
-    basedir = os.path.dirname(__file__)
-    dotfile = os.path.join(basedir,".build_cache_dir")
-    BUILD_CACHE_DIR = ""
-    if os.path.exists(dotfile):
-        BUILD_CACHE_DIR = open(dotfile).readline().strip()
-    BUILD_CACHE_DIR = os.environ.get('BUILD_CACHE_DIR',BUILD_CACHE_DIR)
-
-    if os.path.isdir(BUILD_CACHE_DIR):
-        print("--------------------------------------------------------")
-        print("BUILD CACHE ACTIVATED (V2). be careful, this is experimental.")
-        print("BUILD_CACHE_DIR: " + BUILD_CACHE_DIR )
-        print("--------------------------------------------------------")
-    else:
-        BUILD_CACHE_DIR = None
-
-    # retrieve 2to3 artifacts
-    if sys.version_info[0] >= 3:
-        from lib2to3 import refactor
-        from  hashlib import sha1
-        import shutil
-        import multiprocessing
-        pyver = "%d.%d" % (sys.version_info[:2])
-        fileq = ["pandas"]
-        to_process = dict()
-
-        # retrieve the hashes existing in the cache
-        orig_hashes=dict()
-        post_hashes=dict()
-        for path,dirs,files in os.walk(os.path.join(BUILD_CACHE_DIR,'pandas')):
-            for f in files:
-                s=f.split(".py-")[-1]
-                try:
-                    prev_h,post_h,ver = s.split('-')
-                    if ver == pyver:
-                        orig_hashes[prev_h] = os.path.join(path,f)
-                        post_hashes[post_h] = os.path.join(path,f)
-                except:
-                    pass
-
-        while fileq:
-            f = fileq.pop()
-
-            if os.path.isdir(f):
-                fileq.extend([os.path.join(f,x) for x in os.listdir(f)])
-            else:
-                if not f.endswith(".py"):
-                    continue
-                else:
-                    try:
-                        h = sha1(open(f,"rb").read()).hexdigest()
-                    except IOError:
-                        to_process[h] = f
-                    else:
-                        if h in orig_hashes and not BC_FORCE_OVERWRITE:
-                            src = orig_hashes[h]
-                            if BC_DEBUG:
-                                print("2to3 cache hit %s,%s" % (f,h))
-                            shutil.copyfile(src,f)
-                        elif h not in post_hashes:
-                            # we're not in a dev dir with already processed files
-                            if BC_DEBUG:
-                                print("2to3 cache miss (will process) %s,%s" % (f,h))
-                            to_process[h] = f
-
-        avail_fixes = set(refactor.get_fixers_from_package("lib2to3.fixes"))
-        avail_fixes.discard('lib2to3.fixes.fix_next')
-        t=refactor.RefactoringTool(avail_fixes)
-        if to_process:
-            print("Starting 2to3 refactoring...")
-            for orig_h,f in to_process.items():
-                if BC_DEBUG:
-                    print("2to3 on %s" % f)
-                try:
-                    t.refactor([f],True)
-                    post_h = sha1(open(f, "rb").read()).hexdigest()
-                    cached_fname = f + '-' + orig_h  + '-' + post_h + '-' + pyver
-                    path = os.path.join(BUILD_CACHE_DIR, cached_fname)
-                    pathdir =os.path.dirname(path)
-                    if BC_DEBUG:
-                        print("cache put %s in %s" % (f, path))
-                    try:
-                        os.makedirs(pathdir)
-                    except OSError as exc:
-                        import errno
-                        if exc.errno == errno.EEXIST and os.path.isdir(pathdir):
-                            pass
-                        else:
-                            raise
-
-                    shutil.copyfile(f, path)
-
-                except Exception as e:
-                    print("While processing %s 2to3 raised: %s" % (f,str(e)))
-
-                    pass
-            print("2to3 done refactoring.")
-
-except Exception as e:
-    if not isinstance(e,ZeroDivisionError):
-        print( "Exception: " + str(e))
-    BUILD_CACHE_DIR = None
-
-class CompilationCacheMixin(object):
-    def __init__(self, *args, **kwds):
-        cache_dir = kwds.pop("cache_dir", BUILD_CACHE_DIR)
-        self.cache_dir = cache_dir
-        if  not os.path.isdir(cache_dir):
-            raise Exception("Error: path to Cache directory (%s) is not a dir" % cache_dir)
-
-    def _copy_from_cache(self, hash, target):
-        src = os.path.join(self.cache_dir, hash)
-        if os.path.exists(src) and not BC_FORCE_OVERWRITE:
-            if BC_DEBUG:
-                print("Cache HIT: asked to copy file %s in %s"  %
-                    (src,os.path.abspath(target)))
-            s = "."
-            for d in target.split(os.path.sep)[:-1]:
-                s = os.path.join(s, d)
-                if not os.path.exists(s):
-                    os.mkdir(s)
-            shutil.copyfile(src, target)
-
-            return True
-
-        return False
-
-    def _put_to_cache(self, hash, src):
-        target = os.path.join(self.cache_dir, hash)
-        if BC_DEBUG:
-            print( "Cache miss: asked to copy file from %s to %s" % (src,target))
-        s = "."
-        for d in target.split(os.path.sep)[:-1]:
-            s = os.path.join(s, d)
-            if not os.path.exists(s):
-                os.mkdir(s)
-        shutil.copyfile(src, target)
-
-    def _hash_obj(self, obj):
-        try:
-            return hash(obj)
-        except:
-            raise NotImplementedError("You must override this method")
-
-class CompilationCacheExtMixin(CompilationCacheMixin):
-    def _hash_file(self, fname):
-        from hashlib import sha1
-        f= None
-        try:
-            hash = sha1()
-            hash.update(self.build_lib.encode('utf-8'))
-            try:
-                if sys.version_info[0] >= 3:
-                    import io
-                    f = io.open(fname, "rb")
-                else:
-                    f = open(fname)
-
-                first_line = f.readline()
-                # ignore cython generation timestamp header
-                if "Generated by Cython" not in first_line.decode('utf-8'):
-                    hash.update(first_line)
-                hash.update(f.read())
-                return hash.hexdigest()
-
-            except:
-                raise
-                return None
-            finally:
-                if f:
-                    f.close()
-
-        except IOError:
-            return None
-
-    def _hash_obj(self, ext):
-        from hashlib import sha1
-
-        sources = ext.sources
-        if (sources is None or
-            (not hasattr(sources, '__iter__')) or
-            isinstance(sources, str) or
-                sys.version[0] == 2 and isinstance(sources, unicode)):  # argh
-            return False
-
-        sources = list(sources) + ext.depends
-        hash = sha1()
-        try:
-            for fname in sources:
-                fhash = self._hash_file(fname)
-                if fhash:
-                    hash.update(fhash.encode('utf-8'))
-        except:
-            return None
-
-        return hash.hexdigest()
-
-
-class CachingBuildExt(build_ext, CompilationCacheExtMixin):
-    def __init__(self, *args, **kwds):
-        CompilationCacheExtMixin.__init__(self, *args, **kwds)
-        kwds.pop("cache_dir", None)
-        build_ext.__init__(self, *args, **kwds)
-
-    def build_extension(self, ext, *args, **kwds):
-        ext_path = self.get_ext_fullpath(ext.name)
-        build_path = os.path.join(self.build_lib, os.path.basename(ext_path))
-
-        hash = self._hash_obj(ext)
-        if hash and self._copy_from_cache(hash, ext_path):
-            return
-
-        build_ext.build_extension(self, ext, *args, **kwds)
-
-        hash = self._hash_obj(ext)
-        if os.path.exists(build_path):
-            self._put_to_cache(hash, build_path)  # build_ext
-        if os.path.exists(ext_path):
-            self._put_to_cache(hash, ext_path)  # develop
-
-    def cython_sources(self, sources, extension):
-        import re
-        cplus = self.cython_cplus or getattr(extension, 'cython_cplus', 0) or \
-            (extension.language and extension.language.lower() == 'c++')
-        target_ext = '.c'
-        if cplus:
-            target_ext = '.cpp'
-
-        for i, s in enumerate(sources):
-            if not re.search("\.(pyx|pxi|pxd)$", s):
-                continue
-            ext_dir = os.path.dirname(s)
-            ext_basename = re.sub("\.[^\.]+$", "", os.path.basename(s))
-            ext_basename += target_ext
-            target = os.path.join(ext_dir, ext_basename)
-            hash = self._hash_file(s)
-            sources[i] = target
-            if hash and self._copy_from_cache(hash, target):
-                continue
-            build_ext.cython_sources(self, [s], extension)
-            self._put_to_cache(hash, target)
-
-        sources = [x for x in sources if x.startswith("pandas") or "lib." in x]
-
-        return sources
-
-if BUILD_CACHE_DIR:  # use the cache
-    cmdclass['build_ext'] = CachingBuildExt
-
-try:
-    # recent
-    setuptools_kwargs['use_2to3'] = True if BUILD_CACHE_DIR is None else False
-except:
-    pass
-
-try:
-    # pre eb2234231 , ~ 0.7.0,
-    setuptools_args['use_2to3'] = True if BUILD_CACHE_DIR is None else False
-except:
-    pass
-
-"""
-def main():
-    opd = os.path.dirname
-    opj = os.path.join
-    s= None
-    with open(opj(opd(__file__),"..","setup.py")) as f:
-        s = f.read()
-    if s:
-        if "BUILD CACHE ACTIVATED (V2)" in s:
-            print( "setup.py already wired with V2 build_cache, skipping..")
-        else:
-            SEP="\nsetup("
-            before,after = s.split(SEP)
-            with open(opj(opd(__file__),"..","setup.py"),"wb") as f:
-                f.write((before + shim + SEP + after).encode('ascii'))
-            print("""
-    setup.py was rewritten to use a build cache.
-    Make sure you've put the following in your .bashrc:
-
-    export BUILD_CACHE_DIR=<an existing directory for saving cached files>
-    echo $BUILD_CACHE_DIR > pandas_repo_rootdir/.build_cache_dir
-
-    Once active, build results (compilation, cythonizations and 2to3 artifacts)
-    will be cached in "$BUILD_CACHE_DIR" and subsequent builds should be
-    sped up if no changes requiring recompilation were made.
-
-    Go ahead and run:
-
-    python setup.py clean
-    python setup.py develop
-
-    """)
-
-if __name__ == '__main__':
-    import sys
-    sys.exit(main())
diff --git a/scripts/winbuild_py27.bat b/scripts/winbuild_py27.bat
deleted file mode 100644
index bec67c7e527ed..0000000000000
--- a/scripts/winbuild_py27.bat
+++ /dev/null
@@ -1,2 +0,0 @@
-SET PATH=C:\MinGW\bin;C:\Python27;C:\Python27\Scripts;%PATH%
-python setup.py build -c mingw32 bdist_wininst