CSV 파일을 Pandas DataFrame으로 가져 오기

CSV 파일을 Pandas DataFrame으로 가져 오기

---

CSV 파일에서 pandas DataFrame 으로 읽는 Python 방법은 무엇입니까 (그런 다음 통계 작업에 사용할 수 있고 다른 유형의 열 등을 가질 수 있음)?

내 CSV 파일 "value.txt"에는 다음 내용이 있습니다.

Date,"price","factor_1","factor_2"
2012-06-11,1600.20,1.255,1.548
2012-06-12,1610.02,1.258,1.554
2012-06-13,1618.07,1.249,1.552
2012-06-14,1624.40,1.253,1.556
2012-06-15,1626.15,1.258,1.552
2012-06-16,1626.15,1.263,1.558
2012-06-17,1626.15,1.264,1.572

R에서는 다음을 사용하여이 파일을 읽습니다.

price <- read.csv("value.txt")  

그러면 R data.frame이 반환됩니다.

> price <- read.csv("value.txt")
> price
     Date   price factor_1 factor_2
1  2012-06-11 1600.20    1.255    1.548
2  2012-06-12 1610.02    1.258    1.554
3  2012-06-13 1618.07    1.249    1.552
4  2012-06-14 1624.40    1.253    1.556
5  2012-06-15 1626.15    1.258    1.552
6  2012-06-16 1626.15    1.263    1.558
7  2012-06-17 1626.15    1.264    1.572

동일한 기능을 얻는 Pythonic 방법이 있습니까?

---

구출에 판다 :

import pandas as pd
print pd.read_csv('value.txt')

        Date    price  factor_1  factor_2
0  2012-06-11  1600.20     1.255     1.548
1  2012-06-12  1610.02     1.258     1.554
2  2012-06-13  1618.07     1.249     1.552
3  2012-06-14  1624.40     1.253     1.556
4  2012-06-15  1626.15     1.258     1.552
5  2012-06-16  1626.15     1.263     1.558
6  2012-06-17  1626.15     1.264     1.572

.NET 과 유사한 pandas DataFrame 을 반환 합니다 R's.

---

다음은 Python의 기본 제공 csv 모듈을 사용하는 pandas 라이브러리의 대안 입니다.

import csv
from pprint import pprint
with open('foo.csv', 'rb') as f:
    reader = csv.reader(f)
    headers = reader.next()
    column = {h:[] for h in headers}
    for row in reader:
        for h, v in zip(headers, row):
            column[h].append(v)
    pprint(column)    # Pretty printer

인쇄됩니다

{'Date': ['2012-06-11',
          '2012-06-12',
          '2012-06-13',
          '2012-06-14',
          '2012-06-15',
          '2012-06-16',
          '2012-06-17'],
 'factor_1': ['1.255', '1.258', '1.249', '1.253', '1.258', '1.263', '1.264'],
 'factor_2': ['1.548', '1.554', '1.552', '1.556', '1.552', '1.558', '1.572'],
 'price': ['1600.20',
           '1610.02',
           '1618.07',
           '1624.40',
           '1626.15',
           '1626.15',
           '1626.15']}

---

CSV 파일을 pandas DataFrame으로 읽으려면 pd.read_csv.

But this isn't where the story ends; data exists in many different formats and is stored in different ways so you will often need to pass additional parameters to read_csv to ensure your data is read in properly.

Here's a table listing common scenarios encountered with CSV files along with the appropriate argument you will need to use. You will usually need all or some combination of the arguments below to read in your data.

┌──────────────────────────────────────────────────────────┬─────────────────────────────┬────────────────────────────────────────────────────────┐
│  Scenario                                                │  Argument                   │  Example                                               │
├──────────────────────────────────────────────────────────┼─────────────────────────────┼────────────────────────────────────────────────────────┤
│  Read CSV with different separator¹                      │  sep/delimiter              │  read_csv(..., sep=';')                                │
│  Read CSV with tab/whitespace separator                  │  delim_whitespace           │  read_csv(..., delim_whitespace=True)                  │
│  Fix UnicodeDecodeError while reading²                   │  encoding                   │  read_csv(..., encoding='latin-1')                     │
│  Read CSV without headers³                               │  header and names           │  read_csv(..., header=False, names=['x', 'y', 'z'])    │
│  Specify which column to set as the index⁴               │  index_col                  │  read_csv(..., index_col=[0])                          │
│  Read subset of columns                                  │  usecols                    │  read_csv(..., usecols=['x', 'y'])                     │
│  Numeric data is in European format (eg., 1.234,56)      │  thousands and decimal      │  read_csv(..., thousands='.', decimal=',')             │
└──────────────────────────────────────────────────────────┴─────────────────────────────┴────────────────────────────────────────────────────────┘

Footnotes

1. By default, read_csv uses a C parser engine for performance. The C parser can only handle single character separators. If your CSV has a multi-character separator, you will need to modify your code to use the 'python' engine. You can also pass regular expressions:

   df = pd.read_csv(..., sep=r'\s*\|\s*', engine='python')
   

2. UnicodeDecodeError occurs when the data was stored in one encoding format but read in a different, incompatible one. Most common encoding schemes are 'utf-8' and 'latin-1', your data is likely to fit into one of these.

3. header=False specifies that the first row in the CSV is a data row rather than a header row, and the names=[...] allows you to specify a list of column names to assign to the DataFrame when it is created.

4. "Unnamed: 0" occurs when a DataFrame with an un-named index is saved to CSV and then re-read after. Instead of having to fix the issue while reading, you can also fix the issue when writing by using

   df.to_csv(..., index=False)
   

There are other arguments I've not mentioned here, but these are the ones you'll encounter most frequently.

---

import pandas as pd
df = pd.read_csv('/PathToFile.txt', sep = ',')

This will import your .txt or .csv file into a DataFrame.

---

You can use the csv module found in the python standard library to manipulate CSV files.

example:

import csv
with open('some.csv', 'rb') as f:
    reader = csv.reader(f)
    for row in reader:
        print row

---

%cd C:\Users\asus\Desktop\python
import pandas as pd
df = pd.read_csv('value.txt')
df.head()
    Date    price   factor_1    factor_2
0   2012-06-11  1600.20 1.255   1.548
1   2012-06-12  1610.02 1.258   1.554
2   2012-06-13  1618.07 1.249   1.552
3   2012-06-14  1624.40 1.253   1.556
4   2012-06-15  1626.15 1.258   1.552

---

Try this

import pandas as pd
data=pd.read_csv('C:/Users/Downloads/winequality-red.csv')

Replace the file target location, with where your data set is found, refer this url https://medium.com/@kanchanardj/jargon-in-python-used-in-data-science-to-laymans-language-part-one-12ddfd31592f

---

Note quite as clean, but:

import csv

with open("value.txt", "r") as f:
    csv_reader = reader(f)
    num = '  '
    for row in csv_reader:
        print num, '\t'.join(row)
        if num == '  ':  
            num=0
        num=num+1

Not as compact, but it does the job:

   Date price   factor_1    factor_2
1 2012-06-11    1600.20 1.255   1.548
2 2012-06-12    1610.02 1.258   1.554
3 2012-06-13    1618.07 1.249   1.552
4 2012-06-14    1624.40 1.253   1.556
5 2012-06-15    1626.15 1.258   1.552
6 2012-06-16    1626.15 1.263   1.558
7 2012-06-17    1626.15 1.264   1.572

참고URL : https://stackoverflow.com/questions/14365542/import-csv-file-as-a-pandas-dataframe