Here’s the other one, particularly useful to convert the multiple columns to string instead of just single column:

In [76]: import numpy as np
In [77]: import pandas as pd
In [78]: df = pd.DataFrame({
    ...:     'A': [20, 30.0, np.nan],
    ...:     'B': ["a45a", "a3", "b1"],
    ...:     'C': [10, 5, np.nan]})
    ...: 

In [79]: df.dtypes ## Current datatype
Out[79]: 
A    float64
B     object
C    float64
dtype: object

## Multiple columns string conversion
In [80]: df[["A", "C"]] = df[["A", "C"]].astype(str) 

In [81]: df.dtypes ## Updated datatype after string conversion
Out[81]: 
A    object
B    object
C    object
dtype: object

If you need to convert ALL columns to strings, you can simply use:

df = df.astype(str)

This is useful if you need everything except a few columns to be strings/objects, then go back and convert the other ones to whatever you need (integer in this case):

 df[["D", "E"]] = df[["D", "E"]].astype(int) 

Using .apply() with a lambda conversion function also works in this case:

total_rows['ColumnID'] = total_rows['ColumnID'].apply(lambda x: str(x))

For entire dataframes you can use .applymap().
(but in any case probably .astype() is faster)

pandas >= 1.0: It’s time to stop using astype(str)!

Prior to pandas 1.0 (well, 0.25 actually) this was the defacto way of declaring a Series/column as as string:

# pandas <= 0.25
# Note to pedants: specifying the type is unnecessary since pandas will 
# automagically infer the type as object
s = pd.Series(['a', 'b', 'c'], dtype=str)
s.dtype
# dtype('O')

From pandas 1.0 onwards, consider using "string" type instead.

# pandas >= 1.0
s = pd.Series(['a', 'b', 'c'], dtype="string")
s.dtype
# StringDtype

Here’s why, as quoted by the docs:

1. You can accidentally store a mixture of strings and non-strings in an object dtype array. It’s better to have a dedicated dtype.

2. object dtype breaks dtype-specific operations like DataFrame.select_dtypes(). There isn’t a clear way to select just text
   while excluding non-text but still object-dtype columns.

3. When reading code, the contents of an object dtype array is less clear than 'string'.

See also the section on Behavioral Differences between "string" and object.

Extension types (introduced in 0.24 and formalized in 1.0) are closer to pandas than numpy, which is good because numpy types are not powerful enough. For example NumPy does not have any way of representing missing data in integer data (since type(NaN) == float). But pandas can using Nullable Integer columns.

---

Why should I stop using it?

Accidentally mixing dtypes
The first reason, as outlined in the docs is that you can accidentally store non-text data in object columns.

# pandas <= 0.25
pd.Series(['a', 'b', 1.23])   # whoops, this should have been "1.23"

0       a
1       b
2    1.23
dtype: object

pd.Series(['a', 'b', 1.23]).tolist()
# ['a', 'b', 1.23]   # oops, pandas was storing this as float all the time.

# pandas >= 1.0
pd.Series(['a', 'b', 1.23], dtype="string")

0       a
1       b
2    1.23
dtype: string

pd.Series(['a', 'b', 1.23], dtype="string").tolist()
# ['a', 'b', '1.23']   # it's a string and we just averted some potentially nasty bugs.

Challenging to differentiate strings and other python objects
Another obvious example example is that it’s harder to distinguish between "strings" and "objects". Objects are essentially the blanket type for any type that does not support vectorizable operations.

Consider,

# Setup
df = pd.DataFrame({'A': ['a', 'b', 'c'], 'B': [{}, [1, 2, 3], 123]})
df
 
   A          B
0  a         {}
1  b  [1, 2, 3]
2  c        123

Upto pandas 0.25, there was virtually no way to distinguish that "A" and "B" do not have the same type of data.

# pandas <= 0.25  
df.dtypes

A    object
B    object
dtype: object

df.select_dtypes(object)

   A          B
0  a         {}
1  b  [1, 2, 3]
2  c        123

From pandas 1.0, this becomes a lot simpler:

# pandas >= 1.0
# Convenience function I call to help illustrate my point.
df = df.convert_dtypes()
df.dtypes

A    string
B    object
dtype: object

df.select_dtypes("string")

   A
0  a
1  b
2  c

Readability
This is self-explanatory 😉

---

OK, so should I stop using it right now?

…No. As of writing this answer (version 1.1), there are no performance benefits but the docs expect future enhancements to significantly improve performance and reduce memory usage for "string" columns as opposed to objects. With that said, however, it’s never too early to form good habits!

I usually use this one:

pd['Column'].map(str)

There are four ways to convert columns to string

1. astype(str)
df['column_name'] = df['column_name'].astype(str)

2. values.astype(str)
df['column_name'] = df['column_name'].values.astype(str)

3. map(str)
df['column_name'] = df['column_name'].map(str)

4. apply(str)
df['column_name'] = df['column_name'].apply(str)

Lets see the performance of each type

#importing libraries
import numpy as np
import pandas as pd
import time

#creating four sample dataframes using dummy data
df1 = pd.DataFrame(np.random.randint(1, 1000, size =(10000000, 1)), columns =['A'])
df2 = pd.DataFrame(np.random.randint(1, 1000, size =(10000000, 1)), columns =['A'])
df3 = pd.DataFrame(np.random.randint(1, 1000, size =(10000000, 1)), columns =['A'])
df4 = pd.DataFrame(np.random.randint(1, 1000, size =(10000000, 1)), columns =['A'])

#applying astype(str)
time1 = time.time()
df1['A'] = df1['A'].astype(str)
print('time taken for astype(str) : ' + str(time.time()-time1) + ' seconds')

#applying values.astype(str)
time2 = time.time()
df2['A'] = df2['A'].values.astype(str)
print('time taken for values.astype(str) : ' + str(time.time()-time2) + ' seconds')

#applying map(str)
time3 = time.time()
df3['A'] = df3['A'].map(str)
print('time taken for map(str) : ' + str(time.time()-time3) + ' seconds')

#applying apply(str)
time4 = time.time()
df4['A'] = df4['A'].apply(str)
print('time taken for apply(str) : ' + str(time.time()-time4) + ' seconds')

Output

time taken for astype(str): 5.472359895706177 seconds
time taken for values.astype(str): 6.5844292640686035 seconds
time taken for map(str): 2.3686647415161133 seconds
time taken for apply(str): 2.39758563041687 seconds

map(str) and apply(str) are takes less time compare with remaining two techniques

pandas version: 1.3.5

Updated answer

df['colname'] = df['colname'].astype(str) => this should work by default. But if you create str variable like str = "myString" before using astype(str), this won’t work. In this case, you might want to use the below line.

df['colname'] = df['colname'].astype('str')

===========

(Note: incorrect old explanation)

df['colname'] = df['colname'].astype('str') => converts dataframe column into a string type

df['colname'] = df['colname'].astype(str) => gives an error

1. .map(repr) is very fast

If you want to convert values to strings in a column, consider .map(repr). For multiple columns, consider .applymap(str).

df['col_as_str'] = df['col'].map(repr)

# multiple columns
df[['col1', 'col2']] = df[['col1', 'col2']].applymap(str)
# or
df[['col1', 'col2']] = df[['col1', 'col2']].apply(lambda col: col.map(repr))

In fact, a timeit test shows that map(repr) is 3 times faster than astype(str) (and is faster than any other method mentioned on this page). Even for multiple columns, this runtime difference still holds. The following is the runtime plot of various methods mentioned here.

astype(str) has very little overhead but for larger frames/columns, map/applymap outperforms it.

---

2. Don’t convert to strings in the first place

There’s very little reason to convert a numeric column into strings given pandas string methods are not optimized and often get outperformed by vanilla Python string methods. If not numeric, there are dedicated methods for those dtypes. For example, datetime columns should be converted to strings using pd.Series.dt.strftime().

One way numeric->string seems to be used is in a machine learning context where a numeric column needs to be treated as categorical. In that case, instead of converting to strings, consider other dedicated methods such as pd.get_dummies or sklearn.preprocessing.LabelEncoder or sklearn.preprocessing.OneHotEncoder to process your data instead.

---

3. Use rename to convert column names to specific types

The specific question in the OP is about converting column names to strings, which can be done by rename method:

df = total_rows.pivot_table(columns=['ColumnID'])
df.rename(columns=str).to_dict('records')
# [{'-1': 2, '3030096843': 1, '3030096845': 1}]

---

The code used to produce the above plots:

import numpy as np
from perfplot import plot
plot(
    setup=lambda n: pd.Series(np.random.default_rng().integers(0, 100, size=n)),
    kernels=[lambda s: s.astype(str), lambda s: s.astype("string"), lambda s: s.apply(str), lambda s: s.map(str), lambda s: s.map(repr)],
    labels= ['col.astype(str)', 'col.astype("string")', 'col.apply(str)', 'col.map(str)', 'col.map(repr)'],
    n_range=[2**k for k in range(4, 22)],
    xlabel='Number of rows',
    title='Converting a single column into string dtype',
    equality_check=lambda x,y: np.all(x.eq(y)));
plot(
    setup=lambda n: pd.DataFrame(np.random.default_rng().integers(0, 100, size=(n, 100))),
    kernels=[lambda df: df.astype(str), lambda df: df.astype("string"), lambda df: df.applymap(str), lambda df: df.apply(lambda col: col.map(repr))],
    labels= ['df.astype(str)', 'df.astype("string")', 'df.applymap(str)', 'df.apply(lambda col: col.map(repr))'],
    n_range=[2**k for k in range(4, 18)],
    xlabel='Number of rows in dataframe',
    title='Converting every column of a 100-column dataframe to string dtype',
    equality_check=lambda x,y: np.all(x.eq(y)));

currently i do it like this

df_pg['store_id'] = df_pg['store_id'].astype('string')