使用来自文本文件 Python 的 Pandas 创建两个数据帧

Question

I need to create two dataframes to operate my data and I have thinked about doing it with pandas.

This is the provided data:

class([1,0,0,0],"Small-molecule metabolism ").
class([1,1,0,0],"Degradation ").
class([1,1,1,0],"Carbon compounds ").
function(tb186,[1,1,1,0],'bglS',"beta-glucosidase").
function(tb2202,[1,1,1,0],'cbhK',"carbohydrate kinase").
function(tb727,[1,1,1,0],'fucA',"L-fuculose phosphate aldolase").
function(tb1731,[1,1,1,0],'gabD1',"succinate-semialdehyde dehydrogenase").
function(tb234,[1,1,1,0],'gabD2',"succinate-semialdehyde dehydrogenase").
function(tb501,[1,1,1,0],'galE1',"UDP-glucose 4-epimerase").
function(tb536,[1,1,1,0],'galE2',"UDP-glucose 4-epimerase").
function(tb620,[1,1,1,0],'galK',"galactokinase").
function(tb619,[1,1,1,0],'galT',"galactose-1-phosphate uridylyltransferase C-term").
function(tb618,[1,1,1,0],'galT',"null").
function(tb993,[1,1,1,0],'galU',"UTP-glucose-1-phosphate uridylyltransferase").
function(tb3696,[1,1,1,0],'glpK',"ATP:glycerol 3-phosphotransferase").
function(tb3255,[1,1,1,0],'manA',"mannose-6-phosphate isomerase").
function(tb3441,[1,1,1,0],'mrsA',"phosphoglucomutase or phosphomannomutase").
function(tb118,[1,1,1,0],'oxcA',"oxalyl-CoA decarboxylase").
function(tb3068,[1,1,1,0],'pgmA',"phosphoglucomutase").
function(tb3257,[1,1,1,0],'pmmA',"phosphomannomutase").
function(tb3308,[1,1,1,0],'pmmB',"phosphomannomutase").
function(tb2702,[1,1,1,0],'ppgK',"polyphosphate glucokinase").
function(tb408,[1,1,1,0],'pta',"phosphate acetyltransferase").
function(tb729,[1,1,1,0],'xylB',"xylulose kinase").
function(tb1096,[1,1,1,0],'null',"null").
class([1,1,2,0],"Amino acids and amines ").
function(tb1905,[1,1,2,0],'aao',"D-amino acid oxidase").
function(tb2531,[1,1,2,0],'adi',"ornithine/arginine decarboxylase").
function(tb2780,[1,1,2,0],'ald',"L-alanine dehydrogenase").
function(tb1538,[1,1,2,0],'ansA',"L-asparaginase").
function(tb1001,[1,1,2,0],'arcA',"arginine deiminase").
function(tb753,[1,1,2,0],'mmsA',"methylmalmonate semialdehyde dehydrogenase").
function(tb751,[1,1,2,0],'mmsB',"methylmalmonate semialdehyde oxidoreductase").

And I would like to have something like:

Is it possible with Pandas? Thanks is advance,

Answer 1

Yes it is possible. Bellow is an example.
There are many ways for doing it (some already in other answers). In this example I tried to make the steps clearer in the code.

import io
import pandas as pd

with open("file.txt") as f:
    lines = f.readlines()  # reads your file line by line and returns a list

### sample:
# ['class([1,0,0,0],"Small-molecule metabolism ").\n',
#  'class([1,1,0,0],"Degradation ").\n',
#  'class([1,1,1,0],"Carbon compounds ").\n',
#  'function(tb186,[1,1,1,0],\'bglS\',"beta-glucosidase").\n', ... ]

df1 = []
df2 = []

for line in lines:
    # this transformation will be common to all lines
    line = line.strip(').\n').replace("[", '"[').replace("]", ']"')

    # here we will separate the lines, perform the specific transformation and append them to their specific variable
    if line.startswith("class"):
        line = line.strip("class(")  # specific transform for "class" line
        df1.append(line)
    else:
        line = line.strip("function(")  # specific transform for "function" line
        df2.append(line)

# in this final block we prepare the variable to be read with pandas and read
df1 = "\n".join(df1)  # prepare
df1 = pd.read_csv(
    io.StringIO(df1),  # as pandas expects a file handler, we use io.StringIO
    header=None,  # no headers, they are given "manually"
    names=['id', 'name'],  # headers
)

# the same as before
df2 = "\n".join(df2)
df2 = pd.read_csv(
    io.StringIO(df2),
    header=None,
    names=['orf', 'class', 'genName', 'desc']
)

Answer 2

I make a file with your text. and here's the code. you can repeat it for df_func. enjoy.

cols = ['x','y']
df = pd.read_csv('1.txt',sep='(',names=cols, header=None)
df.head()
df_class = df[df['x']=='class']
df_func = df[df['x']=='function']
df_class[['y', 'z']] =df_class['y'].str.split(',"', 1, expand=True)
df_class['z'] = df_class['z'].str[:-4]
df_class

Answer 3

Read each line of the file, then, for each line:

# line contains the line of the file for this iteration
if line.startswith("class"):
    line.replace("class(","[").replace(").","]")
    line = eval(line)
    # class type stuff
elif line.startswith("function"):
    line.replace("function(","[").replace(").","]")
    line = eval(line)
    # function type stuff

The resulting line variable will be the list of the elements for the line. Then you can do whatever you need with it.

Example: first line = [[1,0,0,0],"Small-molecule metabolism "]