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Converting pandas dataframe to structured arrays


Converting pandas dataframe to structured arrays

By : Ahmad Ayub
Date : November 20 2020, 03:01 PM
Hope that helps Melt the DataFrame to make A and B (the column index) into a column. To get rid of the numeric index, make this new column the index. Then call to_records():
code :
import pandas as pd
a = [2.5,3.3]
b = [3.6,3.9]
D = {'A': a, 'B': b}
df = pd.DataFrame(D)
result = (pd.melt(df, var_name='Type', value_name='Value')
          .set_index('Type').to_records())
print(repr(result))
rec.array([('A',  2.5), ('A',  3.3), ('B',  3.6), ('B',  3.9)], 
          dtype=[('Type', 'O'), ('Value', '<f8')])
In [167]: df
Out[167]: 
     A    B
0  2.5  3.6
1  3.3  3.9

In [168]: pd.melt(df)
Out[168]: 
  variable  value
0        A    2.5
1        A    3.3
2        B    3.6
3        B    3.9
In [169]: pd.melt(df).to_records()
Out[169]: 
rec.array([(0, 'A',  2.5), (1, 'A',  3.3), (2, 'B',  3.6), (3, 'B',  3.9)], 
          dtype=[('index', '<i8'), ('variable', 'O'), ('value', '<f8')])


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If I use python pandas, is there any need for structured arrays?

If I use python pandas, is there any need for structured arrays?


By : Pratik Gandhi
Date : March 29 2020, 07:55 AM
To fix the issue you can do pandas's DataFrame is a high level tool while structured arrays are a very low-level tool, enabling you to interpret a binary blob of data as a table-like structure. One thing that is hard to do in pandas is nested data types with the same semantics as structured arrays, though this can be imitated with hierarchical indexing (structured arrays can't do most things you can do with hierarchical indexing).
Structured arrays are also amenable to working with massive tabular data sets loaded via memory maps (np.memmap). This is a limitation that will be addressed in pandas eventually, though.
Pandas semi structured JSON data frame to simple Pandas dataframe

Pandas semi structured JSON data frame to simple Pandas dataframe


By : vjbdn
Date : March 29 2020, 07:55 AM
this will help Taking your input string above as a variable named 'data', this Python+pyparsing code will make some sense of it. Unfortunately, that stuff to the right of the fourth '|' isn't really JSON. Fortunately, it is well enough formatted that it can be parsed without undue discomfort. See the embedded comments in the program below:
code :
from pyparsing import *
from datetime import datetime

# for the most part, we suppress punctuation - it's important at parse time
# but just gets in the way afterwards
LBRACE,RBRACE,COLON,DBLQ,LBRACK,RBRACK = map(Suppress, '{}:"[]')
DBLQ2 = DBLQ + DBLQ

# define some scalar value expressions, including parse-time conversion parse actions
realnum = Regex(r'[+-]?\d+\.\d*').setParseAction(lambda t:float(t[0]))
integer = Regex(r'[+-]?\d+').setParseAction(lambda t:int(t[0]))
timestamp = Regex(r'""\d{4}-\d{2}-\d{2}T\d{2}:\d{2}""')
timestamp.setParseAction(lambda t: datetime.strptime(t[0][2:-2],'%Y-%m-%dT%H:%M'))
string_value = QuotedString('""')

# define our base key ':' value expression; use a Forward() placeholder
# for now for value, since these things can be recursive
key = Optional(DBLQ2) + Word(alphas, alphanums+'_') + DBLQ2
value = Forward()
key_value = Group(key + COLON + value)

# objects can be values too - use the Dict class to capture keys as field names
obj = Group(Dict(LBRACE + OneOrMore(key_value) + RBRACE))
objlist = (LBRACK + ZeroOrMore(obj) + RBRACK)

# define expression for previously-declared value, using <<= operator
value <<= timestamp | string_value | realnum | integer | obj | Group(objlist)

# the outermost objects are enclosed in "s, and list of them can be given with '|' delims
quotedObj = DBLQ + obj + DBLQ
obsList = delimitedList(quotedObj, delim='|')
fields = data.split('|',4)
result = obsList.parseString(fields[-1])

# we get back a list of objects, dump them out
for r in result:
    print r.dump()
    print
[['currency', 'EUR'], ['item_id', '143'], ['type', 'FLIGHT'], ['name', 'PAR-FEZ'], ['price', 1111], ['origin', 'PAR'], ['destination', 'FEZ'], ['merchant', 'GOV'], ['flight_type', 'OW'], ['flight_segment', [[['origin', 'ORY'], ['destination', 'FEZ'], ['departure_date_time', datetime.datetime(2015, 8, 2, 7, 20)], ['arrival_date_time', datetime.datetime(2015, 8, 2, 9, 5)], ['carrier', 'AT'], ['f_class', 'ECONOMY']]]]]
- currency: EUR
- destination: FEZ
- flight_segment: 
  [0]:
    [['origin', 'ORY'], ['destination', 'FEZ'], ['departure_date_time', datetime.datetime(2015, 8, 2, 7, 20)], ['arrival_date_time', datetime.datetime(2015, 8, 2, 9, 5)], ['carrier', 'AT'], ['f_class', 'ECONOMY']]
    - arrival_date_time: 2015-08-02 09:05:00
    - carrier: AT
    - departure_date_time: 2015-08-02 07:20:00
    - destination: FEZ
    - f_class: ECONOMY
    - origin: ORY
- flight_type: OW
- item_id: 143
- merchant: GOV
- name: PAR-FEZ
- origin: PAR
- price: 1111
- type: FLIGHT

[['type', 'FLIGHT'], ['name', 'FI_ORY-OUD'], ['item_id', 'FLIGHT'], ['currency', 'EUR'], ['price', 111], ['origin', 'ORY'], ['destination', 'OUD'], ['flight_type', 'OW'], ['flight_segment', [[['origin', 'ORY'], ['destination', 'OUD'], ['departure_date_time', datetime.datetime(2015, 8, 2, 13, 55)], ['arrival_date_time', datetime.datetime(2015, 8, 2, 15, 30)], ['flight_number', 'AT625'], ['carrier', 'AT'], ['f_class', 'ECONOMIC_DISCOUNTED']]]]]
- currency: EUR
- destination: OUD
- flight_segment: 
  [0]:
    [['origin', 'ORY'], ['destination', 'OUD'], ['departure_date_time', datetime.datetime(2015, 8, 2, 13, 55)], ['arrival_date_time', datetime.datetime(2015, 8, 2, 15, 30)], ['flight_number', 'AT625'], ['carrier', 'AT'], ['f_class', 'ECONOMIC_DISCOUNTED']]
    - arrival_date_time: 2015-08-02 15:30:00
    - carrier: AT
    - departure_date_time: 2015-08-02 13:55:00
    - destination: OUD
    - f_class: ECONOMIC_DISCOUNTED
    - flight_number: AT625
    - origin: ORY
- flight_type: OW
- item_id: FLIGHT
- name: FI_ORY-OUD
- origin: ORY
- price: 111
- type: FLIGHT
res[0].currency
res[0].price
res[0].destination
res[0].flight_segment[0].origin
len(res[0].flight_segment) # gives how many segments
Converting a Dataframe into a Series with cells containing arrays in Pandas

Converting a Dataframe into a Series with cells containing arrays in Pandas


By : daniela
Date : March 29 2020, 07:55 AM
this one helps. Instantiate a new series using a dict comprehension (this should be faster than an apply based solution).
code :
pd.Series({c : df[c].dropna().unique().tolist() for c in df.columns})

asset             [a]
name     [john, dave]
id          [1, 2, 3]
dtype: object
pd.Series(
    {c : df[c].dropna().unique().tolist() for c in df.columns}
).to_frame().T

  asset          name         id
0   [a]  [john, dave]  [1, 2, 3]
Converting dataframe to structured list

Converting dataframe to structured list


By : Jagadish Hadimani
Date : March 29 2020, 07:55 AM
may help you . In the following .RData file: , You can do
code :
setNames(b$Colour, b$CellType)
#>       Macrophages                DC         Microglia      B cells, pro 
#>         "#E9C825"         "#E77800"         "#E3B60B"         "#EE3900" 
#>                NA       Neutrophils         Monocytes        Mast cells 
#>         "#54A6BA"         "#E39700"         "#7EB8BC"         "#6EB2C2" 
#> Endothelial cells         Basophils           B cells        Stem cells 
#>         "#E7C21C"         "#E1B002"         "#3B9AB2"         "#AEC07B" 
#>           T cells               NKT               ILC               Tgd 
#>         "#E5BC13"         "#61ACBE"         "#96BC9C"         "#C6C55A" 
#>          NK cells  Epithelial cells       Fibroblasts     Stromal cells 
#>         "#EA5800"         "#47A0B6"         "#F21A00"         "#DEC93A" 
class(my_colour[["CellType"]])
#> [1] "character"

attributes(my_colour[["CellType"]])
#> $names
#>  [1] "Macrophages"       "DC"                "Microglia"         "B cells, pro"     
#>  [5] "NA"                "Neutrophils"       "Monocytes"         "Mast cells"       
#>  [9] "Endothelial cells" "Basophils"         "B cells"           "Stem cells"       
#> [13] "T cells"           "NKT"               "ILC"               "Tgd"              
#> [17] "NK cells"          "Epithelial cells"  "Fibroblasts"       "Stromal cells" 
Which Pandas dataframe is better: super long dataframe VS badly structured one with lists

Which Pandas dataframe is better: super long dataframe VS badly structured one with lists


By : user3720480
Date : March 29 2020, 07:55 AM
Any of those help like you mention, having a list inside a column of a dataframe is a bad structure, and long format dataframe is preferred. Let me attempt to answer the question from several aspects:
Added complexity for data manipulation & lack of native support functions for list-like column
code :
df1.groupby(['LABEL','NGRAM']).count().unstack(-1).fillna(0)

df2.explode(column='NGRAM').groupby(['LABEL','NGRAM']).count().unstack(-1).fillna(0)
df1['NGRAM'] = df1['NGRAM'].str.capitalize()
# 1000 loops, best of 5: 1.49 ms per loop

df2['NGRAM'] = df2['NGRAM'].explode().str.capitalize().groupby(level=0).apply(list)
# 1000 loops, best of 5: 246 µs per loop
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