In [16]: df = DataFrame(np.arange(10).reshape(5,2),columns=list('AB'))
In [17]: df
Out[17]:
A B
0 0 1
1 2 3
2 4 5
3 6 7
4 8 9
In [18]: df.dtypes
Out[18]:
A int64
B int64
dtype: object
Convert a series
In [19]: df['A'].apply(str)
Out[19]:
0 0
1 2
2 4
3 6
4 8
Name: A, dtype: object
In [20]: df['A'].apply(str)[0]
Out[20]: '0'
Don't forget to assign the result back:
df['A'] = df['A'].apply(str)
Convert the whole frame
In [21]: df.applymap(str)
Out[21]:
A B
0 0 1
1 2 3
2 4 5
3 6 7
4 8 9
In [22]: df.applymap(str).iloc[0,0]
Out[22]: '0'
df = df.applymap(str)
Top answer 1 of 7
193
In [16]: df = DataFrame(np.arange(10).reshape(5,2),columns=list('AB'))
In [17]: df
Out[17]:
A B
0 0 1
1 2 3
2 4 5
3 6 7
4 8 9
In [18]: df.dtypes
Out[18]:
A int64
B int64
dtype: object
Convert a series
In [19]: df['A'].apply(str)
Out[19]:
0 0
1 2
2 4
3 6
4 8
Name: A, dtype: object
In [20]: df['A'].apply(str)[0]
Out[20]: '0'
Don't forget to assign the result back:
df['A'] = df['A'].apply(str)
Convert the whole frame
In [21]: df.applymap(str)
Out[21]:
A B
0 0 1
1 2 3
2 4 5
3 6 7
4 8 9
In [22]: df.applymap(str).iloc[0,0]
Out[22]: '0'
df = df.applymap(str)
2 of 7
132
Change data type of DataFrame column:
To int:
df.column_name = df.column_name.astype(np.int64)
To str:
df.column_name = df.column_name.astype(str)
Top answer 1 of 2
2
The type object is actually string in pandas dataframe.
If you would like to retain the data as string, use df.to_excel() instead of df.to_csv. This is because when opening the CSV file, Excel will automatically convert the number data to numbers.
df1 = pd.DataFrame({'GL': [2311000200.0, 2312000600.0, 2330800100.0]})
df1.GL = df1.GL.astype('int64').astype('string')
df1.to_excel('test.xlsx', index=False)
2 of 2
1
You can force it to use the string dtype by using:
>>> df1.GL.astype("string")
df1.GL
0 2311000200.0
1 2312000600.0
2 2330800100.0
Name: GL, dtype: string
However, object dtypes are fine for most string operations. As per the docs:
For backwards-compatibility, object dtype remains the default type we infer a list of strings to
pandas - How to convert object data type into int64 in python? - Stack Overflow
I have a dataset and it has one variable as object data type, i have to convert it to int64 type. More on stackoverflow.com
python - Convert columns to string in Pandas - Stack Overflow
I have the following DataFrame from a SQL query: (Pdb) pp total_rows ColumnID RespondentCount 0 -1 2 1 3030096843 1 2 3030096845 1 and I More on stackoverflow.com
pandas - Change of value to the converter Int64 in string Python - Stack Overflow
I have a problem understanding what causes the date value to change when I convert. I have the following value "19670619", type int64. I need to convert to String and leave the date "06/19/1967". ... More on stackoverflow.com
python - Converting dtype('int64') to pandas dataframe - Stack Overflow
Here's my data with type dtype('int64') Portugal 76 Germany 536 Argentina 637 What I need is make it pandas dataframe with, I need this is pandas dataframe Country Count Portugal 76 More on stackoverflow.com
datagy
datagy.io › home › pandas tutorials › pandas dataframes › pandas: convert column values to strings
Pandas: Convert Column Values to Strings • datagy
December 15, 2022 - We can see that our Age column, which was previously stored as int64 is now stored as the string datatype. In the next section, you’ll learn how to use the .map() method to convert a Pandas column values to strings. Similar to the .astype() Pandas series method, you can use the .map() method to convert a Pandas column to strings. ... import pandas as pd df = pd.DataFrame({ 'Name':['Nik', 'Jane', 'Matt', 'Kate', 'Clark'], 'Age': [30, 31, 29, 33, 43], 'Income':[70000, 72000, 83000, 90000, 870000] }) df['Age'] = df['Age'].map(str) print(df.info())
GeeksforGeeks
geeksforgeeks.org › how-to-convert-integers-to-strings-in-pandas-dataframe
How to Convert Integers to Strings in Pandas DataFrame? - GeeksforGeeks
July 1, 2022 - Python3 · # importing pandas as pd import pandas as pd # creating a dictionary of integers dict = {'Integers' : [10, 50, 100, 350, 700]} # creating dataframe from dictionary df = pd.DataFrame.from_dict(dict) print(df) print(df.dtypes) print('\n') # converting each value of column to a string df['Integers'] = df['Integers'].astype(str) print(df) print(df.dtypes) Output : We can see in the above output that before the datatype was int64 and after the conversion to a string, the datatype is an object which represents a string.
Statistics Globe
statisticsglobe.com › home › python programming language for statistics & data science › convert integer to string in pandas dataframe column in python (4 examples)
Convert Integer to String in pandas DataFrame Column (Python Example)
May 2, 2022 - In Example 4, in contrast, I’ll illustrate how to use the apply function instead of the astype function to convert an integer column to the string data type. Consider the Python code below: data_new4 = data.copy() # Create copy of DataFrame ...
TutorialsPoint
tutorialspoint.com › fastest-way-to-convert-integers-to-strings-in-pandas-dataframe
How to Convert String to Integer in Pandas DataFrame?
July 10, 2023 - import pandas as pd df = pd.DataFrame({'Values': ['10', '20', '30', '40']}) # Convert to numeric then to integer df['Values'] = pd.to_numeric(df['Values']).astype(int) print(df.dtypes) print(df) Values int64 dtype: object Values 0 10 1 20 2 30 3 40 · Use astype(int) for clean string data that you're confident contains only valid integers.
Top answer 1 of 3
6
You can try by doing df["Bare Nuclei"].astype(np.int64) but as far as I can see the problem is something else. Pandas first reads all the data to best estimate the data type for each column, then only makes the data frame. So, there must be some entries in the data frame which are not integer types, i.e., they may contain some letters. In that case, also typecasting should give an error. So you need to remove those entries before successfully making the table integer.
2 of 3
0
I had the same problem with the same dataset.
There are lots of "?" in the data for the 'bare_nuclei' column (16) of them in the csv itself you need to use the error handling to drop the rows with the ? in the bare_nuclei column, also as a heads up don't name 'class' column class as that's a reserved keyword in python and that's also going to cause problems later.
You can fix this at import using:
missing_values = ["NA","N/a",np.nan,"?"]
l1 = pd.read_csv("../DataSets/Breast cancer dataset/breast-cancer-wisconsin.data",
header=None, na_values=missing_values,
names=['id','clump_thickness','uniformity_of_cell_size',
'uniformity_of_cell_shape', 'marginal_adhesion',
'single_epithelial_cell_size', 'bare_nuclei', 'bland_chromatin',
'normal_nucleoli', 'mitoses', 'diagnosis'])
l1 = l1.dropna()
GeeksforGeeks
geeksforgeeks.org › fastest-way-to-convert-integers-to-strings-in-pandas-dataframe
Fastest way to Convert Integers to Strings in Pandas DataFrame | GeeksforGeeks
August 1, 2020 - In this article, we'll look at different methods to convert an integer into a string in a Pandas dataframe.
Saturn Cloud
saturncloud.io › blog › how-to-convert-columns-to-string-in-pandas
How to Convert Columns to String in Pandas | Saturn Cloud Blog
December 2, 2023 - Let’s say we have a Pandas DataFrame df that contains a column named employee_id that we want to convert to a string. We can use the following code to do this: # Converting 'employee_id' to string df['employee_id'] = df['employee_id'].astype(str) # Displaying the types of data after conversion print("\nTypes of data after conversion:\n", df.dtypes) ... Types of data after conversion: employee_id object name object age int64 salary int64 experience int64 dtype: object
Spark By {Examples}
sparkbyexamples.com › home › pandas › pandas convert integer to string in dataframe
Pandas Convert Integer to String in DataFrame - Spark By {Examples}
December 5, 2024 - To convert an integer column to a string in a pandas DataFrame, you can use the astype(str) method. Additionally, other Pandas functions like apply(),
Pandas
pandas.pydata.org › docs › reference › api › pandas.DataFrame.convert_dtypes.html
pandas.DataFrame.convert_dtypes — pandas 3.0.3 documentation
Convert the DataFrame to use best possible dtypes. >>> dfn = df.convert_dtypes() >>> dfn a b c d e f 0 1 x True h 10 <NA> 1 2 y False i <NA> 100.5 2 3 z <NA> <NA> 20 200.0 · >>> dfn.dtypes a Int32 b string c boolean d string e Int64 f Float64 dtype: object
Top answer 1 of 10
629
One way to convert to string is to use astype:
total_rows['ColumnID'] = total_rows['ColumnID'].astype(str)
However, perhaps you are looking for the to_json function, which will convert keys to valid json (and therefore your keys to strings):
In [11]: df = pd.DataFrame([['A', 2], ['A', 4], ['B', 6]])
In [12]: df.to_json()
Out[12]: '{"0":{"0":"A","1":"A","2":"B"},"1":{"0":2,"1":4,"2":6}}'
In [13]: df[0].to_json()
Out[13]: '{"0":"A","1":"A","2":"B"}'
Note: you can pass in a buffer/file to save this to, along with some other options...
2 of 10
157
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)
Top answer 1 of 2
1
I solved my problem as follows.
Op['dt'] = pd.to_datetime(Op['dt'], format='%Y%m%d')
Op['dt'] = pd.to_datetime(Op['dt'], unit = 's')
Op['dt'] = Op['dt'].fillna(datetime(2200,12,31))
Op['dt'] = Op['dt'].apply(lambda x: x.strftime('%d/%m/%Y'))
2 of 2
0
Try pd.to_datetime(df["dt"]).dt.strftime('%d/%m/%Y')
Ex:
import pandas as pd
df = pd.DataFrame({"dt" : [ "19670619"]})
df["dt"] = pd.to_datetime(df["dt"]).dt.strftime('%d/%m/%Y')
print(df)
Output:
dt
0 19/06/1967
Delft Stack
delftstack.com › home › howto › python pandas › pandas convert column values to string
How to Convert Column Values to String in Pandas | Delft Stack
February 2, 2024 - DataFrame before Conversion: Name Score Age 0 Ayush 31 33 1 Bikram 38 34 2 Ceela 33 38 3 Kusal 39 45 4 Shanty 35 37 Datatype of columns before conversion: Name object Score int64 Age int64 dtype: object DataFrame after conversion: Name Score Age 0 Ayush 31 33 1 Bikram 38 34 2 Ceela 33 38 3 Kusal 39 45 4 Shanty 35 37 Datatype of columns after conversion: Name object Score int64 Age object dtype: object · It changes the data type of the Age column from int64 to object type representing the string.
Spark By {Examples}
sparkbyexamples.com › home › pandas › pandas convert column to string type
Pandas Convert Column to String Type - Spark By {Examples}
July 3, 2025 - In this article, I will explain how to convert single column or multiple columns to string type in pandas DataFrame, here, I will demonstrate using
Pandas
pandas.pydata.org › pandas-docs › stable › reference › api › pandas.DataFrame.astype.html
pandas.DataFrame.astype — pandas 3.0.1 documentation
This method allows the conversion of the data types of pandas objects, including DataFrames and Series, to the specified dtype. It supports casting entire objects to a single data type or applying different data types to individual columns using a mapping. ... Use a str, numpy.dtype, pandas.ExtensionDtype or Python type to cast entire pandas object to the same type.
Medium
tmlb-blog-ltd.medium.com › to-convert-a-float64-type-column-into-an-int64-or-string-type-column-in-python-short-984537c97a63
To convert a float64-type column into an int64 or string-type column in python (short) | by T Miyamoto | Medium
August 20, 2020 - df_a['coli']= (df_a['colf'].apply(lambda w0: None if pd.isnull(w0) else np.int64(w0)) ).astype(pd.Int64Dtype()) ... Next we create a string type column based on column colf. It is well known that astype(str) converts NaN into a string ‘nan’. We try to do
Python Principles
pythonprinciples.com › blog › converting-integer-to-string-in-python
Converting integer to string in Python – Python Principles
To convert an integer to a string, use the str() built-in function. The function takes an int as input and produces a string as its output. Here are some examples.