I'm using Pandas to load an Excel spreadsheet which contains zip code (e.g. 32771). The zip codes are stored as 5 digit strings in spreadsheet. When they are pulled into a DataFrame using the command...
xls = pd.ExcelFile("5-Digit-Zip-Codes.xlsx")
dfz = xls.parse('Zip Codes')
they are converted into numbers. So '00501' becomes 501.
So my questions are, how do I:
a. Load the DataFrame and keep the string type of the zip codes stored in the Excel file?
b. Convert the numbers in the DataFrame into a five digit string e.g. "501" becomes "00501"?
Method #1: Using compression=zip in pandas. read_csv() method. By assigning the compression argument in read_csv() method as zip, then pandas will first decompress the zip and then will create the dataframe from CSV file present in the zipped file.
If you want to change the data type for all columns in the DataFrame to the string type, you can use df. applymap(str) or df. astype(str) methods.
One of the way to create Pandas DataFrame is by using zip() function. You can use the lists to create lists of tuples and create a dictionary from it. Then, this dictionary can be used to construct a dataframe. zip() function creates the objects and that can be used to produce single item at a time.
gz is not supported by Pandas! See: github.com/pandas-dev/pandas/issues/…
As a workaround, you could convert the int
s to 0-padded strings of length 5 using Series.str.zfill
:
df['zipcode'] = df['zipcode'].astype(str).str.zfill(5)
Demo:
import pandas as pd
df = pd.DataFrame({'zipcode':['00501']})
df.to_excel('/tmp/out.xlsx')
xl = pd.ExcelFile('/tmp/out.xlsx')
df = xl.parse('Sheet1')
df['zipcode'] = df['zipcode'].astype(str).str.zfill(5)
print(df)
yields
zipcode
0 00501
You can avoid panda's type inference with a custom converter, e.g. if 'zipcode'
was the header of the column with zipcodes:
dfz = xls.parse('Zip Codes', converters={'zipcode': lambda x:x})
This is arguably a bug since the column was originally string encoded, made an issue here
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