将日期重新格式化为YYYY / MM / DD并替换CSV中的列数据

Question

I'm a newbie python programmer and I have a CSV that is simple, looks like this:

FROM_ID,JOIN_DATE,FAV_SPORT
100004,06/08/2016,Football
100006,06/08/2016,Tennis
100007,06/08/2016,Football
100009,06/08/2016,Basketball

I am trying to rewrite the date to YYYY/MM/DD. So far I have gotten this far:

import csv
f = open('reg.csv')
csv_f = csv.reader(f)

for row in csv_f:
    parts = row[1].split('/')
print parts[2]

All this does is print out the year (YYYY), which is one step closer :) Can anyone advise how to reformat the parts into the YYYY/MM/DD format?

Also, I notice python doesnt have case/select. How would I create a find/replace on "FROM_ID" and replace them with another number? Like:

if FROM_ID is equal to X then Y

Thanks in advance for any help. I have scoured the internet for hours and I am a touch stuck but hoping I can get moving along. Thanks!

Answer 1

You're pretty close. All you need is:

print parts[2] + "/" + parts[0] + "/" + parts[1]

Answer 2

import csv
f = open('reg.csv')
csv_f = csv.reader(f)

for row in csv_f:
    if "JOIN_DATE" in row: continue
    parts = row[1].split('/')
    data = "{}/{}/{}".format(parts[2],parts[1],parts[0])
print data

Answer 3

As to your second point, you should probably have a look at pandas , which is a Python library for data analysis, especially of tabular data sets.

You could read in your data using

df = pd.read_csv("path_to_your_file")

which would return a DataFrame on which you can do operations such as selecting subsets, your example would become

df[df.FROM_ID == X] 

Answer 4

Try this one:

import datetime
.
.
datetime.datetime.strptime(row[1],'%d/%m/%Y').strftime('%Y/%m/%d')

Answer 5

I reccommend using pandas:

import pandas as pd

df = pd.read_csv("path_to_your_file")

def change_date_format(date):

    # create a list of the substrings separated by '/',
    # so in your case ['DD', 'MM', 'YYYY'] 
    split_dt = date.split("/")

    return split_dt[2] + '/' + split_dt[1] + '/' + split_dt[0]   

#apply this function to all elements of 'JOIN_DATE' columns
df.loc[:, 'JOIN_DATE'] = df.loc[:, 'JOIN_DATE'].apply(change_date_format)

On your second question, you can do:

to_replace = ['X', 'Z']
replace_values = ['Y', 'W']
replace_dict = dict(zip(to_replace, replace_values))

df['FROM_ID'] = df['FROM_ID'].replace(replace_dict)

Answering to the question in the comment, assume you create a csv with two columns: "To_Replace" and "Replace_Value"

     To_Replace    Replace_Value
0          X             Y
1          W             Z
2          A             B

You can create the replace_dict used in the script above like this:

import pandas as pd

replace_file = pd.read_csv(r'C:\Users\flabriol\Desktop\example_so.csv')
replace_dict = dict(zip(replace_file['To_Replace'], replace_file['Replace_Value']))