pandas 熊猫：将多列转换为字符串
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pandas: convert multiple columns to string
提问by As3adTintin
I have some columns ['a', 'b', 'c', etc.](aand care float64while bis object)
我有一些列['a', 'b', 'c', etc.]（a而c在float64同时b为object）
I would like to convert all columns to string and preserve nans.
我想将所有列转换为字符串并保留nans。
Tried using df[['a', 'b', 'c']] == df[['a', 'b', 'c']].astype(str)but that left blanks for the float64columns.
尝试使用，df[['a', 'b', 'c']] == df[['a', 'b', 'c']].astype(str)但为float64列留下了空白。
Currently I am going through one by one with the following:
目前我正在一一处理以下内容：
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)?
最好的方法是使用.astype(str)然后替换''为np.nan？附带问题：.astype(str)和之间有区别.apply(str)吗？
Sample Input:(dtypes: a=float64, b=object, c=float64)
样本输入：（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)
所需的输出：（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.
回答by Alexander
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)
回答by Surya
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.
您可以.astype()对数据框的每个元素应用函数，也可以通过以下方式选择感兴趣的列以转换为字符串。
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
回答by Raj
This gives you the list of column names
这为您提供了列名列表
lst = list(df)
This converts all the columns to string type
这会将所有列转换为字符串类型
df[lst] = df[lst].astype(str)
回答by ahmad fairuz
I did this way.
我是这样做的。
get all your value from a specific column, e.g. 'text'.
从特定列中获取所有值，例如“文本”。
k = df['text'].values
then, run each value into a new declared string, e.g. 'thestring'
然后，将每个值运行到一个新的声明字符串中，例如 '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.
因此，pandas 'text' 列中的所有字符串都已放入一个字符串变量中。
cheers, fairuz
欢呼，费尔鲁兹
