Michael Perdue Michael Perdue - 2 months ago 8
Python Question

pandas: return column values that begin with certain number(s)

I have the following df:

url = 'https://raw.githubusercontent.com/108michael/ms_thesis/master/sic_naics_catcode.csv'
df= pd.read_csv(url, index_col=0)
df.head(3)

SICcode Catcode Category SICname MultSIC 2012 NAICS Code 2002to2007 NAICS
0 111 A1500 Wheat, corn, soybeans and cash grain Wheat X 111140 111140
1 112 A1600 Other commodities (incl rice, peanuts, honey) X 111160 111160
2 115 A1500 Wheat, corn, soybeans and cash grain Corn X 111150 111150


I want to return all rows that begin with e.g., 531, or 92, or, in some cases, values that begin with 5416 through 5419 in column
2002to2007 NAICS
.

I think that this must be quite easy. I'm familiar with (this is just a template)
dz = df[(df['date'] > '01/03/2005') & (df['date'] < '01/03/2015')]
type code, but I don't know about any 'wild-card' symbols that would allow me to enter a truncated value.

Any ideas?

Answer

you can use the RegEx power for that:

df.loc[df['2002to2007 NAICS'].astype(str).str.contains(r'^(?:531|92|541[6-9])')]

will give you all values that begin with 531 or 92 or 5416-5419

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