def get_dataframe_and_info(self,df_name):
cursor = self.db.dataframes.find({'df_name':df_name})
df = [ column for column in cursor ]
dfdict = dict()
rownames = []
info = dict()
# TU MUSI BYĆ SORTOWANIE po kluczu i to kluczu INTEGER!!
for nr,column in enumerate(df): # type(df['columns']) == type([])
# first, get rownames
if nr == 0:
rownames = column['data'].keys()
# Replace decimal point ',' with '.' to convert it later to type 'float'
if column['type'] == u'liczba rzeczywista':
column['data'] = dict((k, v.replace(',','.')) for (k, v) in column['data'].iteritems())
#convert rows from string to INTEGER
rows = dict((int(key), value) for (key, value) in column['data'].items())
# prepare dictionary of columns to convert them into pandas.DataFrame
dfdict[column['name']] = [value for (key,value) in rows.iteritems()]
info[column['name']] = dict((key,value) for key, value in column.iteritems() if key != 'data') # Copy everything but data
psDf = psDataFrame(dfdict, index = rownames )
"""
If codebook states that the column is numeric than convert it to numeric
"""
for column in df['columns']:
if column['type'] == u'liczba całkowita':
psDf[column['name']] = psDf[column['name']].astype(int)
elif column['type'] == u'liczba rzeczywista':
psDf[column['name']] = psDf[column['name']].astype(float)
else:
psDf[column['name']] = psDf[column['name']].astype(str)
# return dictionary for views.py
return {'info':info,'df': psDf}
def process_data(db):
codebook = open("codebook.csv", "r")
df = open ("df.csv","r")
cb_reader = csv.reader(codebook, delimiter = ';', quotechar = '"')
cb_reader.next() ## skip the header of the codebook!
data_reader = csv.reader(df,delimiter=";",quotechar='"')
data_header = data_reader.next() ## get header
df_rows = [row for row in data_reader]
pandas_df = psDataFrame(df_rows,columns=data_header)
insert_query =mongodb_prepare(cb_reader,pandas_df, 'pierszytest')
dataframes = db.dataframes
dataframes.insert(insert_query)
def get_dataframe(df_name):
dataframes = db.dataframes
cursor = db.dataframes.find({'df_name':'pierszytest'})
df = cursor.next()
dfdict = dict()
rownames = []
# TU MUSI BYĆ SORTOWANIE po kluczu i to kluczu INTEGER!!
for nr,column in enumerate(df['columns']): # type(df['columns']) == type([])
print column['name'], " - ",nr
if nr == 0:
rownames = column['data'].keys()
rows = dict((int(key), value) for (key, value) in column['data'].items())
dfdict[column['name']] = [value for (key,value) in rows.iteritems()]
pandas_df = psDataFrame(dfdict, index = rownames )
pandas_df.to_csv("df_out.csv", sep=";", quoting=csv.QUOTE_NONNUMERIC,encoding="utf-8",float_format=".")
def process_data(self, df_name, codebook, df):
"""
get codebook and dataframe, convert df into pandas object and
insert it into mongoDB. This can be much improved :
TODO find a way to chop df into columns w/o pandas and make a json object
directly keeping row numbers.
"""
# check if df_name already exists. If so, throw a ValidationError
self.is_dfname_unique(df_name)
cb_reader = csv.reader(codebook, delimiter=';', quotechar='"')
cb_reader.next() ## skip the header of the codebook!
data_reader = csv.reader(df, delimiter=";", quotechar='"')
data_header = data_reader.next() ## get header
df_rows = [row for row in data_reader]
pandas_df = psDataFrame(df_rows, columns=data_header)
self.mongodb_insert_columns(cb_reader, pandas_df, df_name)
def process_data(self, df_name, codebook, df):
"""
get codebook and dataframe, convert df into pandas object and
insert it into mongoDB. This can be much improved :
TODO find a way to chop df into columns w/o pandas and make a json object
directly keeping row numbers.
"""
# check if df_name already exists. If so, throw a ValidationError
self.is_dfname_unique(df_name)
cb_reader = csv.reader(codebook, delimiter = ';', quotechar = '"')
cb_reader.next() ## skip the header of the codebook!
data_reader = csv.reader(df,delimiter=";",quotechar='"')
data_header = data_reader.next() ## get header
df_rows = [row for row in data_reader]
pandas_df = psDataFrame(df_rows,columns=data_header)
self.mongodb_insert_columns(cb_reader,pandas_df, df_name)
def get_dataframe_and_info(self, df_name):
cursor = self.db.dataframes.find({'df_name': df_name})
df = [column for column in cursor]
dfdict = dict()
rownames = []
info = dict()
# TU MUSI BYĆ SORTOWANIE po kluczu i to kluczu INTEGER!!
for nr, column in enumerate(df): # type(df['columns']) == type([])
# first, get rownames
if nr == 0:
rownames = column['data'].keys()
# Replace decimal point ',' with '.' to convert it later to type 'float'
if column['type'] == u'liczba rzeczywista':
column['data'] = dict((k, v.replace(',', '.'))
for (k, v) in column['data'].iteritems())
#convert rows from string to INTEGER
rows = dict(
(int(key), value) for (key, value) in column['data'].items())
# prepare dictionary of columns to convert them into pandas.DataFrame
dfdict[column['name']] = [
value for (key, value) in rows.iteritems()
]
info[column['name']] = dict(
(key, value) for key, value in column.iteritems()
if key != 'data') # Copy everything but data
psDf = psDataFrame(dfdict, index=rownames)
"""
If codebook states that the column is numeric than convert it to numeric
"""
for column in df['columns']:
if column['type'] == u'liczba całkowita':
psDf[column['name']] = psDf[column['name']].astype(int)
elif column['type'] == u'liczba rzeczywista':
psDf[column['name']] = psDf[column['name']].astype(float)
else:
psDf[column['name']] = psDf[column['name']].astype(str)
# return dictionary for views.py
return {'info': info, 'df': psDf}
