Tukukkk

I have a DataFrame with zip codes, among other things. The data, as a sample, looks like this:

     Zip    Item1     Item2   Item3

 78264.0      pan  elephant    blue

 73909.0  steamer     panda  yellow

  2602.0      pot     rhino  orange

 59661.0     fork     zebra   green

861893.0     sink    ocelot     red

 77892.0  spatula    doggie   brown

Some of these zip codes are invalid, having either too many or too few digits. I'm trying to remove those rows that have an invalid number of characters/digits (seven characters in this case, because I am checking length based on str() and the .0 is included in there). The following lengths loop:

zips = mydata.iloc[:,0].astype(str)

lengths = 

for i in zips:

    lengths.append(len(i))

produces a series (not to be confused with Series, although maybe it is--I'm new at Python) of zip code character lengths for each row. I am then trying to subset the DataFrame based on the information from the lengths variable. I tried a couple of different ways; this following was the latest version:

for i in lengths.index(i):

    if mydata.iloc[i:,0] != 7:

        mydata.iloc[i:,0].drop()

Naturally, this fails, with a ValueError: '44114.0' is not in list error. Can anyone give some advice as to how to do what I'm trying to accomplish?

You can write this more concisely using Pandas filtering rather than loops and ifs.

Here is an example:

valid_zips = mydata[mydata.astype(str).str.len() == 7]

or

zip_code_upper_bound = 100000

valid_zips = mydata[mydata < zip_code_upper_bound]

assuming fractional numbers are not included in your set. Note that the first example will remove shorter zips, while the second will leave them in, which you might want as they could have had leading zeros.

Sample output:

With df defined as (from your example):

        Zip    Item1     Item2   Item3

0   78264.0      pan  elephant    blue

1   73909.0  steamer     panda  yellow

2    2602.0      pot     rhino  orange

3   59661.0     fork     zebra   green

4  861893.0     sink    ocelot     red

5   77892.0  spatula    doggie   brown

Using the following code:

df[df.Zip.astype(str).str.len() == 7]

The result is:

       Zip    Item1     Item2   Item3

0  78264.0      pan  elephant    blue

1  73909.0  steamer     panda  yellow

3  59661.0     fork     zebra   green

5  77892.0  spatula    doggie   brown

Using str.len

df[df.iloc[:,0].astype(str).str.len()!=7]

          A

1  1.222222

2  1.222200

dput :

df=pd.DataFrame({'A':[1.22222,1.222222,1.2222]})

See if this works

df1 = df['ZipCode'].astype(str).map(len)==5