Changing CSV files in python

You can do this with a combination of groupby, replace and a dict

In [76]: from pandas import DataFrame

In [77]: fruits = ['banana', 'apple', 'pear', 'banana', 'watermelon', 'orange', 'apple']

In [78]: times = ['5m', '8m', '6m', '7m', '13m', '2m', '6m']

In [79]: time_map = {'banana': '10m', 'apple': '15m', 'pear': '5m'}

In [80]: df = DataFrame({'fruits': fruits, 'time': times})
Out[80]:
       fruits time
0      banana   5m
1       apple   8m
2        pear   6m
3      banana   7m
4  watermelon  13m
5      orange   2m
6       apple   6m

In [81]: def replacer(g, time_map):
   ....:     tv = g.time.values
   ....:     return g.replace(to_replace=tv, value=time_map.get(g.name, tv))

In [82]: df.groupby('fruits').apply(replacer, time_map)
Out[82]:
       fruits time
0      banana  10m
1       apple  15m
2        pear   5m
3      banana  10m
4  watermelon  13m
5      orange   2m
6       apple  15m

You said you're new to programming so I'll explain what's going on.

1. df.groupby('fruits') splits the DataFrame into subsets (which are DataFrames or Series objects) using the values of the fruits column.

2. The apply method applies a function to each of the aforementioned subsets and concatenates the result (if needed).

3. replacer is where the "magic" happens: each group's time values get replaced (to_replace) with the new value that's defined in time_map. The get method of dicts allows you to provide a default value if the key you're searching for (the fruit name in this case) is not there. nan is commonly used for this purpose, but here I'm actually just using the time that was already there if there isn't a new one defined for it in the time_map dict.

One thing to note is my use of g.name. This doesn't normally exist as an attribute on DataFrames (you can of course define it yourself if you want to), but is there so you can perform computations that may require the group name. In this case that's the "current" fruit you're looking at when you apply your function.

If you have a new value for each fruit or you write in the old values manually you can shorten this to a one-liner:

In [130]: time_map = {'banana': '10m', 'apple': '15m', 'pear': '5m', 'orange': '10m', 'watermelon': '100m'
}

In [131]: s = Series(time_map, name='time')

In [132]: s[df.fruits]
Out[132]:
fruits
banana         10m
apple          15m
pear            5m
banana         10m
watermelon    100m
orange         10m
apple          15m
Name: time, dtype: object

In [133]: s[df.fruits].reset_index()
Out[133]:
       fruits  time
0      banana   10m
1       apple   15m
2        pear    5m
3      banana   10m
4  watermelon  100m
5      orange   10m
6       apple   15m

Assuming that your data is in a Pandas DataFrame and looks something like this:

PN  PRODUCT NUMBER  INV ENT TIME
1    Banana 600000                10m
2    Apple  400000       F4        15m                                              
3    Pair                       6m
4    Banana 4000     G3            10m
5    Watermelon 700000          13m
6    Orange 12000               2m
7    Apple  1650000         15m

Then you should be able to do manipulate values in one column based on values in another column (same row) using simple loops like this:

for numi, i in enumerate(df["PRODUCT"]):
    if i == "Banana":
        df["TIME"][numi] = "10m"
    if i == "Apple":
        df["TIME"][numi] = "15m"

The code first loops through the rows of the dataframe column "PRODUCT", with the row value stored as i and the row-number stored as numi. It then uses if statements to identify the different levels of interest in the Product column. For those rows with the levels of interest (eg "Banana" or "Apple"), it uses the row-numbers to change the value of another column in the same row.

There are lots of ways to do this, and depending on the size of your data and the number of levels (in this case "Products") you want to change, this isn't necessarily the most efficient way to do this. But since you're a beginner, this will probably be a good basic way of doing it for you to start with.