CODEWITHSUNDEEP
pythonstringpandasdataframe
Dheeraj
Dheeraj

Reputation: 1202

Drop row containing string values in any of the multiple columns

For most of you, this question seems easy but for beginners like me, its kind of difficult.

I have a dataframe like this:

Voltage    Current  Kilo Watt   KVA         Power
423.83772   79.33   38.915012   58.08210746 asf
422.90244   33.73   23.3185     27.43352941 0.85
422.57336   92.29   50.75332    67.67109333 0.75
429.29352   38.5    24.63797    28.64880233 0.86
423.61256   86.49   45.14188    63.58011268 0.71
423.61256   86.49   dfhfgj      63.58011268 0.71
422.91976   36.89   21.20996    24.66274419 0.86
427.18048   88.19   45.815193   64.52844085 0.71
427.12852   64.25   21.9954     47.81608696 pqr
422.46944   75.6    36.11695    55.56453846 0.65
426.66088   32.65   20.36856    24.24828571 0.84
406.88144   304.31  85.102684   212.75671   0.4
412.25064   142.62  92.578545   107.6494709 0.86
412.0428    75.5    36.51638    52.92228986 0.69
422.20964   79.3    39.08528    58.33623881 xyz
421.20508   93.71   ggg         67.97310132 0.76
425.82952   136.95  106.97365   534.86825   0.2

I want to drop the respective rows which has string values.

I tried:

df[df != 0.].dropna(axis=0)

This drops the rows with zeros and errors.

how can i achieve this in python?

Upvotes: 0

Views: 423

Answers (2)

aslavkin
aslavkin

Reputation: 224

first convert rows into columns.

df = pd.DataFrame(dict(A=[5,3,5,6], C=[1,"bar","fooXYZbar", "bat"]))
df._get_numeric_data()

result:

A
0  5
1  3
2  5
3  6

Upvotes: 0

Zero
Zero

Reputation: 76917

Here's a one-liner.

In [882]: df.apply(pd.to_numeric, errors='coerce').dropna()
Out[882]:
      Voltage  Current   Kilo Watt         KVA  Power
1   422.90244    33.73   23.318500   27.433529   0.85
2   422.57336    92.29   50.753320   67.671093   0.75
3   429.29352    38.50   24.637970   28.648802   0.86
4   423.61256    86.49   45.141880   63.580113   0.71
6   422.91976    36.89   21.209960   24.662744   0.86
7   427.18048    88.19   45.815193   64.528441   0.71
9   422.46944    75.60   36.116950   55.564538   0.65
10  426.66088    32.65   20.368560   24.248286   0.84
11  406.88144   304.31   85.102684  212.756710   0.40
12  412.25064   142.62   92.578545  107.649471   0.86
13  412.04280    75.50   36.516380   52.922290   0.69
16  425.82952   136.95  106.973650  534.868250   0.20

Upvotes: 1

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