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This isn't an answer to your question -- so I'll make it CW -- but you might be interested in looking at the <a href="http://pandas.pydata.org/" rel="nofollow">pandas</a> library. It makes working with tabular data a lot more fun than it would be otherwise. First read in the data (I'm using your <code>NewCaseFile</code> here, which seems comma-delimited, so I called it <code>ncf.csv</code>): <pre><code>>>> import pandas as pd >>> df = pd.read_csv("ncf.csv") >>> df <class 'pandas.core.frame.DataFrame'> Int64Index: 932 entries, 0 to 931 Data columns (total 9 columns): zcta 932 non-null values xcoord 932 non-null values ycoord 932 non-null values m5064 932 non-null values m6574 932 non-null values m75plus 932 non-null values f5064 932 non-null values f6574 932 non-null values f75plus 932 non-null values dtypes: float64(1), int64(8) >>> df.head() # look at the start of the frame zcta xcoord ycoord m5064 m6574 m75plus f5064 f6574 f75plus 0 51062 211253.4 4733175 0 0 1 0 0 0 1 51011 212255.6 4757939 0 0 1 0 0 0 2 51109 215303.5 4721048 0 1 7 0 1 2 3 51001 215651.1 4746655 1 0 4 0 1 0 4 51103 216887.7 4713568 4 9 28 1 1 8 </code></pre> Use the x,y,zip columns as an index, and sum across the population columns: <pre><code>>>> df = df.set_index(["zcta", "xcoord", "ycoord"]) >>> df["total"] = df.sum(axis=1) >>> df.head() m5064 m6574 m75plus f5064 f6574 f75plus total zcta xcoord ycoord 51062 211253.4 4733175 0 0 1 0 0 0 1 51011 212255.6 4757939 0 0 1 0 0 0 1 51109 215303.5 4721048 0 1 7 0 1 2 11 51001 215651.1 4746655 1 0 4 0 1 0 6 51103 216887.7 4713568 4 9 28 1 1 8 51 </code></pre> Sum by the columns: <pre><code>>>> df.sum() m5064 981 m6574 1243 m75plus 2845 f5064 1355 f6574 1390 f75plus 1938 total 9752 dtype: int64 </code></pre> Et cetera. In particular, it makes it much easier to do many otherwise straightforward-to-explain-but-annoying-in-practice transformations. For example: <pre><code>>>> df = pd.read_csv("ncf.csv") >>> d2 = pd.melt(df, id_vars=list(df.columns[:3])) >>> d2["sex"] = d2["variable"].str[:1] >>> d2["age_lower"] = d2["variable"].str[1:3].astype(float) >>> d2["age_upper"] = d2["variable"].str[3:].replace("plus", 100).astype(float) >>> del d2["variable"] >>> d2.rename(columns={"value": "count"}, inplace=True) </code></pre> gives: <pre><code>>>> d2.head() zcta xcoord ycoord count sex age_lower age_upper 0 51062 211253.4 4733175 0 m 50 64 1 51011 212255.6 4757939 0 m 50 64 2 51109 215303.5 4721048 0 m 50 64 3 51001 215651.1 4746655 1 m 50 64 4 51103 216887.7 4713568 4 m 50 64 >>> d2.groupby("sex")["count"].sum() sex f 4683 m 5069 Name: count, dtype: int64 </code></pre> and so on.
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1. POPython - CSV Reader - For Loop Reading The Rows, But Not Reaching The End of the File
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1. COThat. Is so frickin awesome. Thank you for sharing that. I actually just got a book called "Python For Data Analysis", but have yet to go through it (it deals a lot with pandas and numpy). Thank you, very much, for posting this!
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