CODEWITHSUNDEEP
pandasdataframe
schamonn
schamonn

Reputation: 13

replace any strings with nan in a pandas dataframe

I'm new to pandas and the dataframe-concept. Because of the format of my data (excel-sheets, first row is the name of my data, the second row is the unit) it's a little tricky to handle it in a data frame. The task is to calculate new data from existing columns, e.g. df.['c'] = df['a']**2 + df.['b'] I get: TypeError: unsupported operand type(s) for ** or pow(): 'str' and 'int'

This did work, but is pain to my hands and eyes:

df.['c'] = df['a']
df.['c'] = df['a'].tail(len(df.['a']-1))**2 + df.['b'].tail(len(df.['b'])-1)
df.loc[0,'c'] = 'unit for c'

Is there any way to do this quicker or with less typing? Thanks already schamonn

Upvotes: 0

Views: 297

Answers (2)

Scott Boston
Scott Boston

Reputation: 153460

Let's look at the error mentioned first in this post.

TypeError: unsupported operand type(s) for ** or pow(): 'str' and 'int'

What this error is staying that you are trying to take and string to a power, we can replicate this error using the following example:

df = pd.DataFrame({'a':['1','2','3'],'b':[4,5,6]})

df['a']**2

Output last line of stack trace:

TypeError: unsupported operand type(s) for ** or pow(): 'str' and 'int'

A simple resolution to this if all your a column are numeric representations, then use pd.to_numeric:

pd.to_numeric(df['a'])**2

Output:

0    1
1    4
2    9
Name: a, dtype: int64

Got non-numeric strings also in column a?

Use errors = 'coerce' as a parameter to pd.to_numeric

df = pd.DataFrame({'a':['a','1','2','3'],'b':[4,5,6,7]})

Use:

pd.to_numeric(df['a'], errors='coerce')**2

Output:

0    NaN
1    1.0
2    4.0
3    9.0
Name: a, dtype: float64

Upvotes: 1

schamonn
schamonn

Reputation: 13

this is how I read in the data

Data = pd.read_excel(fileName, sheet_name = 'Messung')
In [154]: Data
Out[154]: 
   T1   T2 Messung                Datum
0  °C   °C       -                    -
1  12  100       1  2018-12-06 00:00:00
2  15  200       2  2018-12-06 00:00:00
3  20  120       3  2018-12-06 00:00:00
4  10  160       4  2018-12-06 00:00:00
5  12  160       5  2018-12-06 00:00:00

Upvotes: 0

Related Questions