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I have some columns ['a', 'b', 'c', etc.] (a and c are float64 while b is object)

I would like to convert all columns to string and preserve nans.

Tried using df[['a', 'b', 'c']] == df[['a', 'b', 'c']].astype(str) but that left blanks for the float64 columns.

Currently I am going through one by one with the following:

df['a'] = df['a'].apply(str) df['a'] = df['a'].replace('nan', np.nan)

Is the best way to use .astype(str) and then replace '' with np.nan? Side question: is there a difference between .astype(str) and .apply(str)?

Sample Input: (dtypes: a=float64, b=object, c=float64)

a, b, c, etc. 23, 'a42', 142, etc. 51, '3', 12, etc. NaN, NaN, NaN, etc. 24, 'a1', NaN, etc.

Desired output: (dtypes: a=object, b=object, c=object)

a, b, c, etc. '23', 'a42', '142', etc. '51', 'a3', '12', etc. NaN, NaN, NaN, etc. '24', 'a1', NaN, etc.
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  • can you post sample input and desired output data sets? Commented May 4, 2016 at 18:25
  • @Maxu for sure, I updated the question Commented May 4, 2016 at 18:29

4 Answers 4

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7

This gives you the list of column names

lst = list(df)

This converts all the columns to string type

df[lst] = df[lst].astype(str)
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4 
df = pd.DataFrame({ 'a': [23.0, 51.0, np.nan, 24.0], 'b': ["a42", "3", np.nan, "a1"], 'c': [142.0, 12.0, np.nan, np.nan]}) for col in df: df[col] = [np.nan if (not isinstance(val, str) and np.isnan(val)) else (val if isinstance(val, str) else str(int(val))) for val in df[col].tolist()] >>> df a b c 0 23 a42 142 1 51 3 12 2 NaN NaN NaN 3 24 a1 NaN >>> df.values array([['23', 'a42', '142'], ['51', '3', '12'], [nan, nan, nan], ['24', 'a1', nan]], dtype=object)
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thanks! so that basically goes through each column, and leaves a np.nan if it is not a string and missing, and otherwise converts the value to string (if i am correct). great! do you know how to get rid of the .0s too?
The columns are converted to floats because of the np.nan. I'll add something to convert to ints.
3

You could apply .astype() function on every elements of dataframe, or could select the column of interest to convert to string by following ways too.

In [41]: df1 = pd.DataFrame({ ...: 'a': [23.0, 51.0, np.nan, 24.0], ...: 'b': ["a42", "3", np.nan, "a1"], ...: 'c': [142.0, 12.0, np.nan, np.nan]}) ...: In [42]: In [42]: df1 Out[42]: a b c 0 23.0 a42 142.0 1 51.0 3 12.0 2 NaN NaN NaN 3 24.0 a1 NaN ### Shows current data type of the columns: In [43]: df1.dtypes Out[43]: a float64 b object c float64 dtype: object ### Applying .astype() on each element of the dataframe converts the datatype to string In [45]: df1.astype(str).dtypes Out[45]: a object b object c object dtype: object ### Or, you could select the column of interest to convert it to strings In [48]: df1[["a", "b", "c"]] = df1[["a","b", "c"]].astype(str) In [49]: df1.dtypes ### Datatype update Out[49]: a object b object c object dtype: object
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Why does df1.astype(str).dtypes show only object types?
0

I did this way.

get all your value from a specific column, e.g. 'text'.

k = df['text'].values

then, run each value into a new declared string, e.g. 'thestring'

thestring = "" for i in range(0,len(k)): thestring += k[i] print(thestring)

hence, all string in column pandas 'text' has been put into one string variable.

cheers, fairuz

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