DMU_lzq1996
DMU_lzq1996

数据加载，存储和文件格式

数据加载，存储和文件格式_第1张图片

import pandas as pd
df = pd.read_csv('examples/ex1.csv')
df
a b c d message
0 1 2 3 4 hello
1 5 6 7 8 world
2 9 10 11 12 foo 
pd.read_table('examples/ex1.csv',sep=",")
a b c d message
0 1 2 3 4 hello
1 5 6 7 8 world
2 9 10 11 12 foo 
pd.read_csv('examples/ex2.csv',header=None)
0 1 2 3 4
0 1 2 3 4 hello
1 5 6 7 8 world
2 9 10 11 12 foo 
pd.read_csv('examples/ex2.csv',names=['a', 'b', 'c', 'd', 'message'])
a b c d message
0 1 2 3 4 hello
1 5 6 7 8 world
2 9 10 11 12 foo 
names = ['a', 'b', 'c', 'd', 'message']
pd.read_csv('examples/ex2.csv',names=names,index_col='message')
a b c d
message
hello 1 2 3 4
world 5 6 7 8
foo 9 10 11 12 
parsed = pd.read_csv('examples/csv_mindex.csv',index_col=['key1','key2'])
parsed
value1 value2
key1 key2
one a 1 2
b 3 4
c 5 6
d 7 8
two a 9 10
b 11 12
c 13 14
d 15 16 
list(open('examples/ex3.txt'))

['            A         B         C\n',
 'aaa -0.264438 -1.026059 -0.619500\n',
 'bbb  0.927272  0.302904 -0.032399\n',
 'ccc -0.264273 -0.386314 -0.217601\n',
 'ddd -0.871858 -0.348382  1.100491\n']

result = pd.read_table('examples/ex3.txt',sep='\s+') #数据规整
result
A B C
aaa -0.264438 -1.026059 -0.619500
bbb 0.927272 0.302904 -0.032399
ccc -0.264273 -0.386314 -0.217601
ddd -0.871858 -0.348382 1.100491 
#用skiprows跳过文件的第一行、第三行和第四行
pd.read_csv('examples/ex4.csv',skiprows=[0,2,3])
a b c d message
0 1 2 3 4 hello
1 5 6 7 8 world
2 9 10 11 12 foo 
#缺失值处理
result = pd.read_csv('examples/ex5.csv')
result
something a b c d message
0 one 1 2 3.0 4 NaN
1 two 5 6 NaN 8 world
2 three 9 10 11.0 12 foo 
pd.isnull(result)
something a b c d message
0 False False False False False True
1 False False False True False False
2 False False False False False False 
result = pd.read_csv('examples/ex5.csv',na_values=[12])#设为缺失值
result
something a b c d message
0 one 1 2 3.0 4.0 NaN
1 two 5 6 NaN 8.0 world
2 three 9 10 11.0 NaN foo 
sentinels = {'message': ['foo', 'NA'], 'something': ['two']}
pd.read_csv('examples/ex5.csv',na_values=sentinels)
something a b c d message
0 one 1 2 3.0 4 NaN
1 NaN 5 6 NaN 8 world
2 three 9 10 11.0 12 NaN

数据加载，存储和文件格式_第2张图片
数据加载，存储和文件格式_第3张图片

#逐块读取文本文件
pd.options.display.max_rows = 10
result = pd.read_csv('examples/ex6.csv')
result
one two three four key
0 0.467976 -0.038649 -0.295344 -1.824726 L
1 -0.358893 1.404453 0.704965 -0.200638 B
2 -0.501840 0.659254 -0.421691 -0.057688 G
3 0.204886 1.074134 1.388361 -0.982404 R
4 0.354628 -0.133116 0.283763 -0.837063 Q
... ... ... ... ... ...
9995 2.311896 -0.417070 -1.409599 -0.515821 L
9996 -0.479893 -0.650419 0.745152 -0.646038 E
9997 0.523331 0.787112 0.486066 1.093156 K
9998 -0.362559 0.598894 -1.843201 0.887292 G
9999 -0.096376 -1.012999 -0.657431 -0.573315 0

10000 rows × 5 columns

pd.read_csv('examples/ex6.csv',nrows=5)  #指定读取
one two three four key
0 0.467976 -0.038649 -0.295344 -1.824726 L
1 -0.358893 1.404453 0.704965 -0.200638 B
2 -0.501840 0.659254 -0.421691 -0.057688 G
3 0.204886 1.074134 1.388361 -0.982404 R
4 0.354628 -0.133116 0.283763 -0.837063 Q 
chunker = pd.read_csv('examples/ex6.csv',chunksize=1000)
chunker     #迭代器



tot = pd.Series([])
for piece in chunker:
    tot = tot.add(piece['key'].value_counts(),fill_value=0)
tot = tot.sort_values(ascending=False)
tot[:10]

E    368.0
X    364.0
L    346.0
O    343.0
Q    340.0
M    338.0
J    337.0
F    335.0
K    334.0
H    330.0
dtype: float64

#将数据写到文本格式
data = pd.read_csv('examples/ex5.csv')
data
something a b c d message
0 one 1 2 3.0 4 NaN
1 two 5 6 NaN 8 world
2 three 9 10 11.0 12 foo 
data.to_csv('examples/out.csv')   #DataFrame方法

import sys
data.to_csv(sys.stdout,sep='|') #这里直接写出到sys.stdout，所以仅仅是打印出文本结果而已

|something|a|b|c|d|message
0|one|1|2|3.0|4|
1|two|5|6||8|world
2|three|9|10|11.0|12|foo

data.to_csv(sys.stdout,na_rep="NULL")

,something,a,b,c,d,message
0,one,1,2,3.0,4,NULL
1,two,5,6,NULL,8,world
2,three,9,10,11.0,12,foo

data.to_csv(sys.stdout,index=False,header=False)

one,1,2,3.0,4,
two,5,6,,8,world
three,9,10,11.0,12,foo

data.to_csv(sys.stdout,index=False,columns=['a','b','c'])

a,b,c
1,2,3.0
5,6,
9,10,11.0

import numpy as np
dates = pd.date_range('1/1/2000',periods=7)
ts = pd.Series(np.arange(7),index =dates)
ts.to_csv('examples/tseries.csv')  #Series的to_csv方法

#处理分隔符格式,为了使数据格式合乎要求
import csv
f = open('examples/ex7.csv')
reader = csv.reader(f)

for line in reader:
    print(line)

['a', 'b', 'c']
['1', '2', '3']
['1', '2', '3']

with open('examples/ex7.csv')as f:
    lines = list(csv.reader(f))
header,values = lines[0],lines[1:]    #读取到列表
data_dict = {h:v for h,v in zip(header,zip(*values))}
data_dict#用字典构造式和zip(*values)，后者将行转置为列，创建数据列的字典

{'a': ('1', '1'), 'b': ('2', '2'), 'c': ('3', '3')}

class my_dialect(csv.Dialect):          #自定义读取格式
    lineterminator = '\n'
    delimiter = ';'
    quotechar = '"'
    quoting = csv.QUOTE_MINIMAL
reader = csv.reader(f,dialect=my_dialect)

数据加载，存储和文件格式_第4张图片

#JSON数据,JSON（JavaScript Object Notation的简称）已经成为通过HTTP请求在Web浏览器和其他应用程序之间发送数据的标准格式之一
obj = """
{"name": "Wes",
 "places_lived": ["United States", "Spain", "Germany"],
 "pet": null,
 "siblings": [{"name": "Scott", "age": 30, "pets": ["Zeus", "Zuko"]},
              {"name": "Katie", "age": 38,
               "pets": ["Sixes", "Stache", "Cisco"]}]
}
"""
import json
result = json.loads(obj)   #将JSON字符串转换成Python形式
result


{'name': 'Wes',
 'places_lived': ['United States', 'Spain', 'Germany'],
 'pet': None,
 'siblings': [{'name': 'Scott', 'age': 30, 'pets': ['Zeus', 'Zuko']},
  {'name': 'Katie', 'age': 38, 'pets': ['Sixes', 'Stache', 'Cisco']}]}

#json.dumps则将Python对象转换成JSON格式：
#asjson = json.dumps(result)

siblings = pd.DataFrame(result['siblings'],columns=['name','age'])
siblings
name age
0 Scott 30
1 Katie 38 
data = pd.read_json('examples/example.json')
data #pandas.read_json的默认选项假设JSON数组中的每个对象是表格中的一行
a b c
0 1 2 3
1 4 5 6
2 7 8 9 
#XML和HTML:Web信息采集
tables = pd.read_html('examples/fdic_failed_bank_list.html')
len(tables)

1

failures = tables[0]
failures.head()
Bank Name City ST CERT Acquiring Institution Closing Date Updated Date
0 Allied Bank Mulberry AR 91 Today's Bank September 23, 2016 November 17, 2016
1 The Woodbury Banking Company Woodbury GA 11297 United Bank August 19, 2016 November 17, 2016
2 First CornerStone Bank King of Prussia PA 35312 First-Citizens Bank & Trust Company May 6, 2016 September 6, 2016
3 Trust Company Bank Memphis TN 9956 The Bank of Fayette County April 29, 2016 September 6, 2016
4 North Milwaukee State Bank Milwaukee WI 20364 First-Citizens Bank & Trust Company March 11, 2016 June 16, 2016 
#利用lxml.objecttify解析XML
from lxml import objectify
path = 'datasets/mta_perf/Performance_MNR.xml'
parsed = objectify.parse(open(path)) #利用lxml.objectify解析该文件
root = parsed.getroot()
data=[]
skip_fields = ['PARENT_SEQ', 'INDICATOR_SEQ',
               'DESIRED_CHANGE', 'DECIMAL_PLACES']
for elt in root.INDICATOR:
    el_data = {}
    for child in elt.getchildren():
        if child.tag in skip_fields:
            continue
        el_data[child.tag] = child.pyval
    data.append(el_data)
perf = pd.DataFrame(data)
perf.head()
AGENCY_NAME CATEGORY DESCRIPTION FREQUENCY INDICATOR_NAME INDICATOR_UNIT MONTHLY_ACTUAL MONTHLY_TARGET PERIOD_MONTH PERIOD_YEAR YTD_ACTUAL YTD_TARGET
0 Metro-North Railroad Service Indicators Percent of commuter trains that arrive at thei... M On-Time Performance (West of Hudson) % 96.9 95 1 2008 96.9 95
1 Metro-North Railroad Service Indicators Percent of commuter trains that arrive at thei... M On-Time Performance (West of Hudson) % 95 95 2 2008 96 95
2 Metro-North Railroad Service Indicators Percent of commuter trains that arrive at thei... M On-Time Performance (West of Hudson) % 96.9 95 3 2008 96.3 95
3 Metro-North Railroad Service Indicators Percent of commuter trains that arrive at thei... M On-Time Performance (West of Hudson) % 98.3 95 4 2008 96.8 95
4 Metro-North Railroad Service Indicators Percent of commuter trains that arrive at thei... M On-Time Performance (West of Hudson) % 95.8 95 5 2008 96.6 95 
from io import StringIO
tag = 'Google'
root = objectify.parse(StringIO(tag)).getroot()
root



root.get('href')

'http://www.google.com'

root.text

'Google'

#二进制数据格式,使用Python内置的pickle序列化
frame = pd.read_csv('examples/ex1.csv')
frame
a b c d message
0 1 2 3 4 hello
1 5 6 7 8 world
2 9 10 11 12 foo 
frame.to_pickle('examples/frame_pickle')#将数据以pickle格式保存到磁盘

pd.read_pickle('examples/frame_pickle')#直接读取被pickle化的数据
a b c d message
0 1 2 3 4 hello
1 5 6 7 8 world
2 9 10 11 12 foo 
#HDF5格式
#HDF5是一种存储大规模科学数组数据的非常好的文件格式

#读取Micosoft Excel文件
#ExcelFile类或pandas.read_excel函数
xlsx = pd.ExcelFile('examples/ex1.xlsx')
xlsx



pd.read_excel(xlsx,'Sheet1')
a b c d message
0 1 2 3 4 hello
1 5 6 7 8 world
2 9 10 11 12 foo 
#直接传递文件名
frame = pd.read_excel('examples/ex1.xlsx','Sheet1')
frame
a b c d message
0 1 2 3 4 hello
1 5 6 7 8 world
2 9 10 11 12 foo 
#pandas数据写入为Excel格式
writer = pd.ExcelWriter('examples/ex2.xlsx')
frame.to_excel(writer,'Sheet1')
writer.save()

#传递文件的路径到to_excel
frame.to_excel('examples/ex2.xlsx')

#Web APls交互
#通过python访问API的方法：request包
import requests
url = 'https://api.github.com/repos/pandas-dev/pandas/issues'
resp = requests.get(url)
resp



data = resp.json()

data #data中的每个元素都是一个包含所有GitHub主题页数据（不包含评论）的字典

[{'url': 'https://api.github.com/repos/pandas-dev/pandas/issues/23369',
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  'title': 'memory usage after writing then reading a multi-indexed dataframe from an hdf5 increases dramatically',
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  'body': "#### Code Sample, a copy-pastable example if possible\r\n\r\n```python\r\n#!/usr/bin/env python\r\n\r\nimport numpy as np\r\nimport pandas as pd\r\n\r\nindex = pd.MultiIndex.from_product([list(range(10000)), list(range(10))])\r\ndf = pd.DataFrame(np.random.rand(100000, 10), index=index, columns=list(range(10)))\r\ndf = (df * 3840).astype(np.int16)\r\ndf.info()\r\n\r\nsdf = df.sample(1000)\r\nsdf.info()\r\n\r\nwith pd.HDFStore('test.h5') as h5:\r\n    h5['sdf'] = sdf\r\n\r\nsdf = pd.read_hdf('test.h5', 'sdf')\r\nsdf.info()\r\n```\r\n\r\n#### Problem description\r\n\r\nThe memory used by a multi-indexed dataframe increases dramatically after writing to and reading it back from an hdf5 data file.\r\n\r\n#### Expected Output\r\n\r\n```\r\nclass 'pandas.core.frame.DataFrame'>\r\nMultiIndex: 100000 entries, (0, 0) to (9999, 9)\r\nData columns (total 10 columns):\r\n0    100000 non-null int16\r\n1    100000 non-null int16\r\n2    100000 non-null int16\r\n3    100000 non-null int16\r\n4    100000 non-null int16\r\n5    100000 non-null int16\r\n6    100000 non-null int16\r\n7    100000 non-null int16\r\n8    100000 non-null int16\r\n9    100000 non-null int16\r\ndtypes: int16(10)\r\nmemory usage: 2.6 MB\r\n\r\n\r\nMultiIndex: 1000 entries, (6381, 5) to (7551, 4)\r\nData columns (total 10 columns):\r\n0    1000 non-null int16\r\n1    1000 non-null int16\r\n2    1000 non-null int16\r\n3    1000 non-null int16\r\n4    1000 non-null int16\r\n5    1000 non-null int16\r\n6    1000 non-null int16\r\n7    1000 non-null int16\r\n8    1000 non-null int16\r\n9    1000 non-null int16\r\ndtypes: int16(10)\r\nmemory usage: 100.7 KB\r\n\r\n\r\nMultiIndex: 1000 entries, (6381, 5) to (7551, 4)\r\nData columns (total 10 columns):\r\n0    1000 non-null int16\r\n1    1000 non-null int16\r\n2    1000 non-null int16\r\n3    1000 non-null int16\r\n4    1000 non-null int16\r\n5    1000 non-null int16\r\n6    1000 non-null int16\r\n7    1000 non-null int16\r\n8    1000 non-null int16\r\n9    1000 non-null int16\r\ndtypes: int16(10)\r\nmemory usage: 421.0 KB\r\n```\r\n\r\n- Note that the sparse version before writing to an hdf5 file uses 100kB, but it uses 421kB after I reread it from the hdf5 file.  This is over 4X the original memory usage.\r\n\r\n- As a note, the original array uses 2.6 MB to store 100000 * 10 * 2 = 2MB of array data (30% overhead), the sparse version before writing uses 100.7kB to store 1000*10*2 = 20kB of array data (400% overhead), and the final version, after rereading from the hd5 file uses 421kB to store 20kB of data (\r\n2000% overhead).\r\n\r\n- A simpler, non-multiindex case gave the same result before and after writing the data to an hdf5 file, with about 30% overhead.\r\n\r\n#### Output of ``pd.show_versions()``\r\n\r\n
\r\nINSTALLED VERSIONS                \r\n------------------                \r\ncommit: None                      \r\npython: 3.6.3.final.0             \r\npython-bits: 64                   \r\nOS: Linux                         \r\nOS-release: 4.13.0-41-generic     \r\nmachine: x86_64                   \r\nprocessor: x86_64                 \r\nbyteorder: little                 \r\nLC_ALL: None                      \r\nLANG: en_US.UTF-8                 \r\nLOCALE: en_US.UTF-8               \r\n\r\npandas: 0.23.4                    \r\npytest: None                      \r\npip: 10.0.1                       \r\nsetuptools: 32.3.1                \r\nCython: None                      \r\nnumpy: 1.14.5                     \r\nscipy: 1.1.0                      \r\npyarrow: None                     \r\nxarray: None                      \r\nIPython: 6.4.0                    \r\nsphinx: None                      \r\npatsy: None                       \r\ndateutil: 2.7.3                   \r\npytz: 2018.5                      \r\nblosc: None                       \r\nbottleneck: None                  \r\ntables: 3.4.4                     \r\nnumexpr: 2.6.8                    \r\nfeather: None                     \r\nmatplotlib: 2.2.2                 \r\nopenpyxl: None                    \r\nxlrd: None                        \r\nxlwt: None                        \r\nxlsxwriter: None                  \r\nlxml: None                        \r\nbs4: None                         \r\nhtml5lib: 1.0.1                   \r\nsqlalchemy: None                  \r\npymysql: None                     \r\npsycopg2: None                    \r\njinja2: 2.10                      \r\ns3fs: None                        \r\nfastparquet: None                 \r\npandas_gbq: None                  \r\npandas_datareader: None           \r\n
\r\n"},
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  'body': "For `Series[period]`, `pd.concat` is about 6x slower than `PeriodArray._concat_same_type`. There's always going to be some overhead, but I  wonder how much we can narrow this.\r\n\r\n```python\r\nIn [1]: import numpy as np\r\n   ...: import pandas as pd\r\n   ...:\r\n   ...: a = np.random.randint(2000, 2100, size=1000)\r\n   ...: b = np.random.randint(2000, 2100, size=1000)\r\n   ...:\r\n   ...: x = pd.core.arrays.period_array(a, freq='B')\r\n   ...: y = pd.core.arrays.period_array(b, freq='B')\r\n   ...:\r\n   ...: s = pd.Series(x)\r\n   ...: t = pd.Series(y)\r\n\r\n\r\nIn [2]: %timeit pd.concat([s, t], ignore_index=True)\r\n523 µs ± 22.6 µs per loop (mean ± std. dev. of 7 runs, 1000 loops each)\r\n\r\nIn [3]: %timeit x._concat_same_type([x, y])\r\n90.1 µs ± 948 ns per loop (mean ± std. dev. of 7 runs, 10000 loops each)\r\n```"},
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  'body': "So I have been working on issue #23154 trying to setup flake8-rst as additional check for code within documentation.\r\n\r\nIt's working great, finding multiple places to fix, however in order to test I'm not able to run the appropriate commands.\r\n\r\n* created a conda environment\r\n* installed dependencies from `./ci/requirements_dev.txt` and `./ci/requirements-optional-conda.txt`\r\n* activated pandas-dev environment\r\n* executed `python make.py html` within `./doc` folder \r\n\r\nResulting in \r\n```\r\n~/.../pandas/core/algorithms.py in _factorize_array(values, na_sentinel, size_hint, na_value)\r\n    472     table = hash_klass(size_hint or len(values))\r\n    473     labels, uniques = table.factorize(values, na_sentinel=na_sentinel,\r\n--> 474                                       na_value=na_value)\r\n    475 \r\n    476     labels = ensure_platform_int(labels)\r\n\r\nTypeError: factorize() takes no keyword arguments\r\n```\r\nSo I can't really evaluate weather the changes I am going to make are correct.\r\n\r\nTrying to build pandas or running the test suite never worked for me as well. Maybe it's just my own error, but I think there are others getting these issues.\r\n\r\nAs the project is already featuring `tox` I'm proposing to enhance tox with all these use-cases so that after a fresh clone of the project someone can just type `tox -e docs` and tox creates a venv with all required dependencies and runs the build,\r\n`tox -e check` to run flake8, isort, validate_docstrings.py ...\r\n`tox` for a complete check of everything -> reducing failing ci-builds\r\n\r\nIn my opinion this could simplify someones first steps to contribute to pandas. What do you think?\r\n\r\n"},
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  'body': 'problem description : \r\nThere is no function available in pandas which can fill missing value in dataframe according to column type i.e whether column is of categorical type or numeric type.\r\nIt will be easier if we provide a direct function which can fill missing value according to the type of column\r\n'},
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  'body': "#### Code Sample, a copy-pastable example if possible\r\nInitializing an `IntervalTree`  with arrays containing `np.nan` can trigger a `RuntimeWarning`:\r\n```python\r\nIn [2]: left, right = [0, 1, 2, np.nan], [1, 2, 3, np.nan]\r\n\r\nIn [3]: tree = pd._libs.interval.IntervalTree(left, right, leaf_size=2)\r\n/home/jeremy/anaconda3/lib/python3.6/site-packages/numpy/lib/function_base.py:4033: RuntimeWarning: Invalid value encountered in median\r\n  r = func(a, **kwargs)\r\n```\r\nThis causes some attributes to be incorrectly set as `np.nan`, which can lead to incorrect results:\r\n```python\r\nIn [4]: tree.get_loc(0.5)\r\nOut[4]: array([0, 1, 2, 3])\r\n```\r\n#### Problem description\r\n\r\nInitializing an `IntervalTree` with data containing `np.nan` triggers a `RuntimeWarning` and can lead to incorrect results.\r\n\r\n#### Expected Output\r\nI'd expect no warnings to be raised and for the `get_loc` query to be correct.\r\n\r\n#### Output of ``pd.show_versions()``\r\n\r\n
\r\n\r\nINSTALLED VERSIONS\r\n------------------\r\ncommit: 437f31cb609372f465aaa853bf4b2299a9550716\r\npython: 3.6.5.final.0\r\npython-bits: 64\r\nOS: Linux\r\nOS-release: 4.14.29-galliumos\r\nmachine: x86_64\r\nprocessor: x86_64\r\nbyteorder: little\r\nLC_ALL: None\r\nLANG: en_US.UTF-8\r\nLOCALE: en_US.UTF-8\r\n\r\npandas: 0.24.0.dev0+824.g437f31c\r\npytest: 3.5.1\r\npip: 18.0\r\nsetuptools: 39.1.0\r\nCython: 0.28.2\r\nnumpy: 1.14.3\r\nscipy: 1.1.0\r\npyarrow: None\r\nxarray: None\r\nIPython: 6.4.0\r\nsphinx: 1.7.4\r\npatsy: 0.5.0\r\ndateutil: 2.7.3\r\npytz: 2018.4\r\nblosc: None\r\nbottleneck: 1.2.1\r\ntables: 3.4.3\r\nnumexpr: 2.6.5\r\nfeather: None\r\nmatplotlib: 2.2.2\r\nopenpyxl: 2.5.3\r\nxlrd: 1.1.0\r\nxlwt: 1.3.0\r\nxlsxwriter: 1.0.4\r\nlxml: 4.2.1\r\nbs4: 4.6.0\r\nhtml5lib: 1.0.1\r\nsqlalchemy: 1.2.7\r\npymysql: None\r\npsycopg2: None\r\njinja2: 2.10\r\ns3fs: None\r\nfastparquet: None\r\npandas_gbq: None\r\npandas_datareader: None\r\ngcsfs: None\r\n\r\n
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  'body': "#### Code Sample, a copy-pastable example if possible\r\n\r\n```python\r\nimport pandas as pd\r\npd.set_option('display.max_rows', -1)\r\ntest = [['a', 'b'] ,['c','d'] ,['f','g']]\r\npd.DataFrame(test, columns=['columna', 'columnb'])\r\n```\r\n#### Problem description\r\n\r\nThis incorrectly print's 4 rows:\r\n\r\n```\r\nOut[1]:\r\n..     ...     ...\r\n   columna columnb\r\n0        a       b\r\n1        c       d\r\n1        c       d\r\n2        f       g\r\n\r\n[3 rows x 2 columns]\r\n```\r\nPandas should not allow negative numbers for this setting because it does non-sensical things. In many other places -1 is used to represent an unbounded number so setting it -1 is actually intuitive but it doesn't work correctly and we should fail fast instead of confusing users.\r\n\r\n#### Expected Output\r\n\r\n```\r\nOut[1]:\r\n..     ...     ...\r\n   columna columnb\r\n0        a       b\r\n1        c       d\r\n2        f       g\r\n\r\n[3 rows x 2 columns]\r\n```\r\n\r\n#### Output of ``pd.show_versions()``\r\n\r\n
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data[0]['title']

'memory usage after writing then reading a multi-indexed dataframe from an hdf5 increases dramatically'

issues = pd.DataFrame(data,columns=['number','title','labels','state'])
issues
number title labels state
0 23369 memory usage after writing then reading a mult... [] open
1 23368 REF: Cleanups, typing, memoryviews in tslibs [] open
2 23367 Fix import format at pandas/tests/io/parser di... [{'id': 106935113, 'node_id': 'MDU6TGFiZWwxMDY... open
3 23366 STY: proposed isort settings [ci skip] [skip c... [] open
4 23365 Fix import format at pandas/tests/extension di... [{'id': 106935113, 'node_id': 'MDU6TGFiZWwxMDY... open
5 23364 Isort contributing guide [{'id': 134699, 'node_id': 'MDU6TGFiZWwxMzQ2OT... open
6 23363 COMPAT: update for latest flake8 [] open
7 23362 PERF: concat perf [{'id': 8935311, 'node_id': 'MDU6TGFiZWw4OTM1M... open
8 23361 unpin openpyxl [] open
9 23359 PERF: Investigate caching on PeriodArray [{'id': 8935311, 'node_id': 'MDU6TGFiZWw4OTM1M... open
10 23358 CI: rebalance the travis ci jobs [{'id': 48070600, 'node_id': 'MDU6TGFiZWw0ODA3... open
11 23356 ENH: Tox as main entry point for development [] open
12 23355 DOC: fixup whatsnew note for GH21394 [] open
13 23354 issue regarding fill missing value [] open
14 23353 BUG: Fix IntervalTree handling of NaN [{'id': 76811, 'node_id': 'MDU6TGFiZWw3NjgxMQ=... open
15 23352 BUG: IntervalTree should not have NaN in nodes [{'id': 76811, 'node_id': 'MDU6TGFiZWw3NjgxMQ=... open
16 23351 Fix import format pandas/tests/indexing [{'id': 106935113, 'node_id': 'MDU6TGFiZWwxMDY... open
17 23349 API/TST: make hasnans always return python boo... [{'id': 31404521, 'node_id': 'MDU6TGFiZWwzMTQw... open
18 23348 Pandas should validate display.max_rows option [{'id': 42670965, 'node_id': 'MDU6TGFiZWw0MjY3... open
19 23347 Run Isort on tests/util single PR [{'id': 106935113, 'node_id': 'MDU6TGFiZWwxMDY... open
20 23346 Run Isort on tests/arrays single PR [{'id': 106935113, 'node_id': 'MDU6TGFiZWwxMDY... open
21 23345 BUG: Fix date_range overflow [{'id': 76811, 'node_id': 'MDU6TGFiZWw3NjgxMQ=... open
22 23344 Run Isort on tests/series single pr [{'id': 106935113, 'node_id': 'MDU6TGFiZWwxMDY... open
23 23343 Run Isort on tests/indexes P4/Final [{'id': 106935113, 'node_id': 'MDU6TGFiZWwxMDY... open
24 23342 Fix+Test Timedelta.__mul__(nan) [{'id': 47223669, 'node_id': 'MDU6TGFiZWw0NzIy... open
25 23340 Run Isort on tests/indexes part three [{'id': 106935113, 'node_id': 'MDU6TGFiZWwxMDY... open
26 23339 Fix import format tests/indexes/multi Part Two [{'id': 106935113, 'node_id': 'MDU6TGFiZWwxMDY... open
27 23338 DOC: Added MultiIndex Example for Series Min [{'id': 134699, 'node_id': 'MDU6TGFiZWwxMzQ2OT... open
28 23337 Fix import format tests/indexes/ "Part One" [{'id': 106935113, 'node_id': 'MDU6TGFiZWwxMDY... open
29 23336 Partialy fix issue #23334 - isort pandas/core/... [{'id': 106935113, 'node_id': 'MDU6TGFiZWwxMDY... open 
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