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So. We have a messy data stored in a TSV file that I need to analyse. This is how it looks
status=200 protocol=http region_name=Podolsk datetime=2016-03-10 15:51:58 user_ip=0.120.81.243 user_agent=Mozilla/5.0 (Windows NT 6.1; WOW64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/48.0.2564.116 Safari/537.36 user_id=7885299833141807155 user_vhost=tindex.ru method=GET page=/search/
And the problem is that some of the rows have different column order / some of them missing values and I need to get rid of that with high performance (since the datasets I am working with are up to 100 Gigabytes).
Data = pd.read_table('data/data.tsv', sep='\t+',header=None,names=['status', 'protocol',\
'region_name', 'datetime',\
'user_ip', 'user_agent',\
'user_id', 'user_vhost',\
'method', 'page'], engine='python')
Clean_Data = (Data.dropna()).reset_index(drop=True)
Now I got rid of missing values but one problem still remains!
This is how the data looks:
And this is how the problem looks:
As you can see some of columns are offset. I made a very low-performance solution
ids = Clean_Data.index.tolist()
for column in Clean_Data.columns:
for row, i in zip(Clean_Data[column], ids):
if np.logical_not(str(column) in row):
Clean_Data.drop([i], inplace=True)
ids.remove(i)
So now the data looks good... at least I can work with it! But what is the High-Performance ALTERNATIVE to the method I made above?
Update on unutbu code: traceback error
---------------------------------------------------------------------------
TypeError Traceback (most recent call last)
<ipython-input-4-52c9d76f9744> in <module>()
8 df.index.names = ['index', 'num']
9
---> 10 df = df.set_index('field', append=True)
11 df.index = df.index.droplevel(level='num')
12 df = df['value'].unstack(level=1)
/Users/Peter/anaconda/lib/python2.7/site-packages/pandas/core/frame.pyc in set_index(self, keys, drop, append, inplace, verify_integrity)
2805 if isinstance(self.index, MultiIndex):
2806 for i in range(self.index.nlevels):
-> 2807 arrays.append(self.index.get_level_values(i))
2808 else:
2809 arrays.append(self.index)
/Users/Peter/anaconda/lib/python2.7/site-packages/pandas/indexes/multi.pyc in get_level_values(self, level)
664 values = _simple_new(filled, self.names[num],
665 freq=getattr(unique, 'freq', None),
--> 666 tz=getattr(unique, 'tz', None))
667 return values
668
/Users/Peter/anaconda/lib/python2.7/site-packages/pandas/indexes/range.pyc in _simple_new(cls, start, stop, step, name, dtype, **kwargs)
124 return RangeIndex(start, stop, step, name=name, **kwargs)
125 except TypeError:
--> 126 return Index(start, stop, step, name=name, **kwargs)
127
128 result._start = start
/Users/Peter/anaconda/lib/python2.7/site-packages/pandas/indexes/base.pyc in __new__(cls, data, dtype, copy, name, fastpath, tupleize_cols, **kwargs)
212 if issubclass(data.dtype.type, np.integer):
213 from .numeric import Int64Index
--> 214 return Int64Index(data, copy=copy, dtype=dtype, name=name)
215 elif issubclass(data.dtype.type, np.floating):
216 from .numeric import Float64Index
/Users/Peter/anaconda/lib/python2.7/site-packages/pandas/indexes/numeric.pyc in __new__(cls, data, dtype, copy, name, fastpath, **kwargs)
105 # with a platform int
106 if (dtype is None or
--> 107 not issubclass(np.dtype(dtype).type, np.integer)):
108 dtype = np.int64
109
TypeError: data type "index" not understood
Pandas version : 0.18.0-np110py27_0
Update
Everything worked... Thanks everybody!
333
Suppose you had TSV data such as this:
status=A protocol=B region_name=C datetime=D user_ip=E user_agent=F user_id=G
user_id=G status=A region_name=C user_ip=E datetime=D user_agent=F protocol=B
protocol=B datetime=D status=A user_ip=E user_agent=F user_id=G
The order of the fields may be scambled, and there may be missing values. However, you don't have to drop rows just because the fields don't appear in a certain order. You can use the field names provided in the row data itself to place the values in the correct columns. For example,
import pandas as pd
df = pd.read_table('data/data.tsv', sep='\t+',header=None, engine='python')
df = df.stack().str.extract(r'([^=]*)=(.*)', expand=True).dropna(axis=0)
df.columns = ['field', 'value']
df = df.set_index('field', append=True)
df.index = df.index.droplevel(level=1)
df = df['value'].unstack(level=1)
print(df)
yields
field datetime protocol region_name status user_agent user_id user_ip
index
0 D B C A F G E
1 D B C A F G E
2 D B None A F G E
To handle a large TSV file, you could process rows in chunks, and then concatenate the processed chunks into one DataFrame at the end:
import pandas as pd
chunksize = # the number of rows to be processed per iteration
dfs = []
reader = pd.read_table('data/data.tsv', sep='\t+',header=None, engine='python',
iterator=True, chunksize=chunksize)
for df in reader:
df = df.stack().str.extract(r'([^=]*)=(.*)', expand=True).dropna(axis=0)
df.columns = ['field', 'value']
df.index.names = ['index', 'num']
df = df.set_index('field', append=True)
df.index = df.index.droplevel(level='num')
df = df['value'].unstack(level=1)
dfs.append(df)
df = pd.concat(dfs, ignore_index=True)
print(df)
Explanation: Given df:
In [527]: df = pd.DataFrame({0: ['status=A', 'user_id=G', 'protocol=B'],
1: ['protocol=B', 'status=A', 'datetime=D'],
2: ['region_name=C', 'region_name=C', 'status=A'],
3: ['datetime=D', 'user_ip=E', 'user_ip=E'],
4: ['user_ip=E', 'datetime=D', 'user_agent=F'],
5: ['user_agent=F', 'user_agent=F', 'user_id=G'],
6: ['user_id=G', 'protocol=B', None]}); df
.....: .....: .....: .....: .....: .....: .....:
Out[527]:
0 1 2 3 4 5 6
0 status=A protocol=B region_name=C datetime=D user_ip=E user_agent=F user_id=G
1 user_id=G status=A region_name=C user_ip=E datetime=D user_agent=F protocol=B
2 protocol=B datetime=D status=A user_ip=E user_agent=F user_id=G None
you can coalesce all the values into a single column
In [449]: df.stack()
Out[449]:
0 0 status=A
1 protocol=B
2 region_name=C
3 datetime=D
4 user_ip=E
5 user_agent=F
6 user_id=G
1 0 user_id=G
1 status=A
2 region_name=C
3 user_ip=E
4 datetime=D
5 user_agent=F
6 protocol=B
2 0 protocol=B
1 datetime=D
2 status=A
3 user_ip=E
4 user_agent=F
5 user_id=G
dtype: object
and then apply .str.extract(r'([^=]*)=(.*)') to separate the field name from the value:
In [450]: df = df.stack().str.extract(r'([^=]*)=(.*)', expand=True).dropna(axis=0); df
Out[450]:
0 1
0 0 status A
1 protocol B
2 region_name C
3 datetime D
4 user_ip E
5 user_agent F
6 user_id G
1 0 user_id G
1 status A
2 region_name C
3 user_ip E
4 datetime D
5 user_agent F
6 protocol B
2 0 protocol B
1 datetime D
2 status A
3 user_ip E
4 user_agent F
5 user_id G
To make it easier to reference parts of the DataFrame, let's give the columns and index levels descriptive names:
In [530]: df.columns = ['field', 'value']; df.index.names = ['index', 'num']; df
Out[530]:
field value
index num
0 0 status A
1 protocol B
...
Now if we move the field column into the index:
In [531]: df = df.set_index('field', append=True); df
Out[531]:
value
index num field
0 0 status A
1 protocol B
2 region_name C
3 datetime D
...
and drop the num index level:
In [532]: df.index = df.index.droplevel(level='num'); df
Out[532]:
value
index field
0 status A
protocol B
region_name C
datetime D
...
then we can obtain a DataFrame of the desired form
by moving the field index level into the column index:
In [533]: df = df['value'].unstack(level=1); df
Out[533]:
field datetime protocol region_name status user_agent user_id user_ip
index
0 D B C A F G E
1 D B C A F G E
2 D B None A F G E
You can use Pandas' vectorized string operations, specifically str.contains:
import numpy as np
# boolean index of rows to keep
is_valid = np.ones(Clean_data.shape[0], np.bool)
for column in Clean_Data.columns:
# check whether rows contain this column name
is_valid &= Clean_Data[column].str.contains(column)
# drop rows where not all elements contain their respective column names
Clean_Data.drop(np.where(~is_valid)[0], inplace=True)
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