Delete rows of a pandas data frame having string values in python 3.4.1 - python-3.x

Question

I have read a csv file with pandas read_csv having 8 columns. Each column may contain int/string/float values. But I want to remove those rows having string values and return a data frame with only numeric values in it. Attaching the csv sample.
I have tried to run this following code:
import pandas as pd
import numpy as np
df = pd.read_csv('new200_with_errors.csv',dtype={'Geo_Level_1' : int,'Geo_Level_2' : int,'Geo_Level_3' : int,'Product_Level_1' : int,'Product_Level_2' : int,'Product_Level_3' : int,'Total_Sale' : float})
print(df)
but I get the following error:
TypeError: unorderable types: NoneType() > int()
I am running with python 3.4.1.
Here is the sample csv.
Geo_L_1,Geo_L_2,Geo_L_3,Pro_L_1,Pro_L_2,Pro_L_3,Date,Sale
1, 2, 3, 129, 1, 5193316745, 1/1/2012, 9
1 ,2, 3, 129, 1, 5193316745, 1/1/2013,
1, 2, 3, 129, 1, 5193316745, , 8
1, 2, 3, 129, NA, 5193316745, 1/10/2012, 10
1, 2, 3, 129, 1, 5193316745, 1/10/2013, 4
1, 2, 3, ghj, 1, 5193316745, 1/10/2014, 6
1, 2, 3, 129, 1, 5193316745, 1/11/2012, 4
1, 2, 3, 129, 1, ghgj, 1/11/2013, 2
1, 2, 3, 129, 1, 5193316745, 1/11/2014, 6
1, 2, 3, 129, 1, 5193316745, 1/12/2012, ghgj
1, 2, 3, 129, 1, 5193316745, 1/12/2013, 5

Answer 1

So the way I would approach this is to try to convert the columns to an int using a user function with a Try/Catch to handle the situation where the value cannot be coerced into an Int, these get set to NaN values. Drop the row where you have an empty value, for some reason it actually has a length of 1 when I tested this with your data, it may work for you using len 0.
In [42]:
# simple function to try to convert the type, returns NaN if the value cannot be coerced
def func(x):
try:
return int(x)
except ValueError:
return NaN
# assign multiple columns
df['Pro_L_1'], df['Pro_L_3'], df['Sale'] = df['Pro_L_1'].apply(func), df['Pro_L_3'].apply(func), df['Sale'].apply(func)
# drop the 'empty' date row, take a copy() so we don't get a warning
df = df.loc[df['Date'].str.len() > 1].copy()
# convert the string to a datetime, if we didn't drop the row it would set the empty row to today's date
df['Date']= pd.to_datetime(df['Date'])
# now convert all the dtypes that are numeric to a numeric dtype
df = df.convert_objects(convert_numeric=True)
# check the dtypes
df.dtypes
Out[42]:
Geo_L_1 int64
Geo_L_2 int64
Geo_L_3 int64
Pro_L_1 float64
Pro_L_2 float64
Pro_L_3 float64
Date datetime64[ns]
Sale float64
dtype: object
In [43]:
# display the current situation
df
Out[43]:
Geo_L_1 Geo_L_2 Geo_L_3 Pro_L_1 Pro_L_2 Pro_L_3 Date Sale
0 1 2 3 129 1 5193316745 2012-01-01 9
1 1 2 3 129 1 5193316745 2013-01-01 NaN
3 1 2 3 129 NaN 5193316745 2012-01-10 10
4 1 2 3 129 1 5193316745 2013-01-10 4
5 1 2 3 NaN 1 5193316745 2014-01-10 6
6 1 2 3 129 1 5193316745 2012-01-11 4
7 1 2 3 129 1 NaN 2013-01-11 2
8 1 2 3 129 1 5193316745 2014-01-11 6
9 1 2 3 129 1 5193316745 2012-01-12 NaN
10 1 2 3 129 1 5193316745 2013-01-12 5
In [44]:
# drop the rows
df.dropna()
Out[44]:
Geo_L_1 Geo_L_2 Geo_L_3 Pro_L_1 Pro_L_2 Pro_L_3 Date Sale
0 1 2 3 129 1 5193316745 2012-01-01 9
4 1 2 3 129 1 5193316745 2013-01-10 4
6 1 2 3 129 1 5193316745 2012-01-11 4
8 1 2 3 129 1 5193316745 2014-01-11 6
10 1 2 3 129 1 5193316745 2013-01-12 5
For the last line assign it so df = df.dropna()