Deleting rows with unwanted values in a Dataframe in python

Question

I need to drop rows in a dataframe created using panda in Python. I've tried all the different ways mentioned in different forums but nothing seems to work.

I want to delete the rows with ZipCodes more than 5 digits long, so I saw this as an answer:

df = df[len(df.CoulmnName) <5]
df.head

and I get Error: "KeyError: False" And I want to iterate through the dataframe to delete rows where there are values greater than 1 in different columns. This is what I tried:

for x in cleandata4bestdeal.columns:
if x[line] > 1:
   df.drop(df[line])

and it doesn't work. I want to iterate through the column headers to remove the rows with values that are not 1's or 0's.

cs95 · Accepted Answer · 2017-11-12 21:26:46Z

1

len(df.ZipCode) is going to give you the column length, not the length of each row. If you want the latter, you'll need to perform the str.len operation on it -

mask = df.ZipCode.astype(str).str.len() <= 5  # retain zipCodes with 5 or less digits

Now, with the mask, you can filter out rows you don't wish to keep:

df = df.loc[mask]  # df = df[mask]

answered Nov 12, 2017 at 21:26

cs95

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8 Comments

isra abuhasna Over a year ago

I did what you wrote but it only gave me the column header as an output, Idk why?

cs95 Over a year ago

@israabuhasna Maybe because you don't have any zips <= 5? Can you post some of your data?

isra abuhasna Over a year ago

Nevermind it worked, so I can use this to iterate over the column headers with the condition that non of the values go above 1?

cs95 Over a year ago

@israabuhasna Iteration? Nah you don't need to iterate. Just df = df[mask] will remove all rows you don't need.

isra abuhasna Over a year ago

@Coldspeed Yeah but now I only want 1's or 0's in the columns after ZipCode. So i want to iterate through the column names to delete any wrong data rows: clmlst = df.columns.tolist() for x in clmlst[2,:]: df = df[(df[x] <= 1)] I'm still getting errors

|

BENY · Accepted Answer · 2017-11-12 21:25:15Z

1

By using .str.len()

df[df.zip.astype(str).str.len()<5]
Out[336]: 
    zip
0  1111
2  1111
3  2222
4  3333

Data input

df=pd.DataFrame({'zip':[1111,111111,1111,2222,3333]})

answered Nov 12, 2017 at 21:25

BENY

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