I have Sr.no columns in my csv file witch contain all integer values but will reading it as pandas data frame some integer values are converted into float why?
I have Data set contain following records
When i load it as Data Frame it shows like this
These are n th records of same data set
But this time in Data Frame SR.NO column it is showing float values
This is type domination.
Check this example:
df = pd.DataFrame([[1, 2], [3, 4]], columns=list('AB'))
A B
0 1 2
1 3 4 <---- ALL INTEGERS
and:
df2 = pd.DataFrame([[np.nan, 6], [7, 8]], columns=list('AB'))
A B
0 NaN 6
1 7.0 8 <-- NOT INTEGER
You can see, 7 -> 7.0.
And more:
df.append(df2, ignore_index=True)
A B
0 1.0 2
1 3.0 4
2 NaN 6
3 7.0 8
Pandas automaticly define a column's type.
For change this, use pd.read_csv(..., dtype={'PUT_COL_NAME_HERE': PUT_TYPE_HERE}) or pd.astype()
