Change column type in pandas

Question

I created a DataFrame from a list of lists:

table = [
    ['a',  '1.2',  '4.2' ],
    ['b',  '70',   '0.03'],
    ['x',  '5',    '0'   ],
]

df = pd.DataFrame(table)

How do I convert the columns to specific types? In this case, I want to convert columns 2 and 3 into floats.

Is there a way to specify the types while converting the list to DataFrame? Or is it better to create the DataFrame first and then loop through the columns to change the dtype for each column? Ideally I would like to do this in a dynamic way because there can be hundreds of columns, and I don't want to specify exactly which columns are of which type. All I can guarantee is that each column contains values of the same type.

Regalado

Answer 1

You have four main options for converting types in pandas:

1. to_numeric() - provides functionality to safely convert non-numeric types (e.g. strings) to a suitable numeric type. (See also to_datetime() and to_timedelta().)

2. astype() - convert (almost) any type to (almost) any other type (even if it's not necessarily sensible to do so). Also allows you to convert to categorial types (very useful).

3. infer_objects() - a utility method to convert object columns holding Python objects to a pandas type if possible.

4. convert_dtypes() - convert DataFrame columns to the "best possible" dtype that supports pd.NA (pandas' object to indicate a missing value).

Read on for more detailed explanations and usage of each of these methods.

---

1. to_numeric()

The best way to convert one or more columns of a DataFrame to numeric values is to use pandas.to_numeric().

This function will try to change non-numeric objects (such as strings) into integers or floating-point numbers as appropriate.

Basic usage

The input to to_numeric() is a Series or a single column of a DataFrame.

>>> s = pd.Series(["8", 6, "7.5", 3, "0.9"]) # mixed string and numeric values
>>> s
0      8
1      6
2    7.5
3      3
4    0.9
dtype: object

>>> pd.to_numeric(s) # convert everything to float values
0    8.0
1    6.0
2    7.5
3    3.0
4    0.9
dtype: float64

As you can see, a new Series is returned. Remember to assign this output to a variable or column name to continue using it:

# convert Series
my_series = pd.to_numeric(my_series)

# convert column "a" of a DataFrame
df["a"] = pd.to_numeric(df["a"])

You can also use it to convert multiple columns of a DataFrame via the apply() method:

# convert all columns of DataFrame
df = df.apply(pd.to_numeric) # convert all columns of DataFrame

# convert just columns "a" and "b"
df[["a", "b"]] = df[["a", "b"]].apply(pd.to_numeric)

As long as your values can all be converted, that's probably all you need.

Error handling

But what if some values can't be converted to a numeric type?

to_numeric() also takes an errors keyword argument that allows you to force non-numeric values to be NaN, or simply ignore columns containing these values.

Here's an example using a Series of strings s which has the object dtype:

>>> s = pd.Series(['1', '2', '4.7', 'pandas', '10'])
>>> s
0         1
1         2
2       4.7
3    pandas
4        10
dtype: object

The default behaviour is to raise if it can't convert a value. In this case, it can't cope with the string 'pandas':

>>> pd.to_numeric(s) # or pd.to_numeric(s, errors='raise')
ValueError: Unable to parse string

Rather than fail, we might want 'pandas' to be considered a missing/bad numeric value. We can coerce invalid values to NaN as follows using the errors keyword argument:

>>> pd.to_numeric(s, errors='coerce')
0     1.0
1     2.0
2     4.7
3     NaN
4    10.0
dtype: float64

The third option for errors is just to ignore the operation if an invalid value is encountered:

>>> pd.to_numeric(s, errors='ignore')
# the original Series is returned untouched

This last option is particularly useful for converting your entire DataFrame, but don't know which of our columns can be converted reliably to a numeric type. In that case, just write:

df.apply(pd.to_numeric, errors='ignore')

The function will be applied to each column of the DataFrame. Columns that can be converted to a numeric type will be converted, while columns that cannot (e.g. they contain non-digit strings or dates) will be left alone.

Downcasting

By default, conversion with to_numeric() will give you either an int64 or float64 dtype (or whatever integer width is native to your platform).

That's usually what you want, but what if you wanted to save some memory and use a more compact dtype, like float32, or int8?

to_numeric() gives you the option to downcast to either 'integer', 'signed', 'unsigned', 'float'. Here's an example for a simple series s of integer type:

>>> s = pd.Series([1, 2, -7])
>>> s
0    1
1    2
2   -7
dtype: int64

Downcasting to 'integer' uses the smallest possible integer that can hold the values:

>>> pd.to_numeric(s, downcast='integer')
0    1
1    2
2   -7
dtype: int8

Downcasting to 'float' similarly picks a smaller than normal floating type:

>>> pd.to_numeric(s, downcast='float')
0    1.0
1    2.0
2   -7.0
dtype: float32

---

2. astype()

The astype() method enables you to be explicit about the dtype you want your DataFrame or Series to have. It's very versatile in that you can try and go from one type to any other.

Basic usage

Just pick a type: you can use a NumPy dtype (e.g. np.int16), some Python types (e.g. bool), or pandas-specific types (like the categorical dtype).

Call the method on the object you want to convert and astype() will try and convert it for you:

# convert all DataFrame columns to the int64 dtype
df = df.astype(int)

# convert column "a" to int64 dtype and "b" to complex type
df = df.astype({"a": int, "b": complex})

# convert Series to float16 type
s = s.astype(np.float16)

# convert Series to Python strings
s = s.astype(str)

# convert Series to categorical type - see docs for more details
s = s.astype('category')

Notice I said "try" - if astype() does not know how to convert a value in the Series or DataFrame, it will raise an error. For example, if you have a NaN or inf value you'll get an error trying to convert it to an integer.

As of pandas 0.20.0, this error can be suppressed by passing errors='ignore'. Your original object will be returned untouched.

Be careful

astype() is powerful, but it will sometimes convert values "incorrectly". For example:

>>> s = pd.Series([1, 2, -7])
>>> s
0    1
1    2
2   -7
dtype: int64

These are small integers, so how about converting to an unsigned 8-bit type to save memory?

>>> s.astype(np.uint8)
0      1
1      2
2    249
dtype: uint8

The conversion worked, but the -7 was wrapped round to become 249 (i.e. 2⁸ - 7)!

Trying to downcast using pd.to_numeric(s, downcast='unsigned') instead could help prevent this error.

---

3. infer_objects()

Version 0.21.0 of pandas introduced the method infer_objects() for converting columns of a DataFrame that have an object datatype to a more specific type (soft conversions).

For example, here's a DataFrame with two columns of object type. One holds actual integers and the other holds strings representing integers:

>>> df = pd.DataFrame({'a': [7, 1, 5], 'b': ['3','2','1']}, dtype='object')
>>> df.dtypes
a    object
b    object
dtype: object

Using infer_objects(), you can change the type of column 'a' to int64:

>>> df = df.infer_objects()
>>> df.dtypes
a     int64
b    object
dtype: object

Column 'b' has been left alone since its values were strings, not integers. If you wanted to force both columns to an integer type, you could use df.astype(int) instead.

---

4. convert_dtypes()

Version 1.0 and above includes a method convert_dtypes() to convert Series and DataFrame columns to the best possible dtype that supports the pd.NA missing value.

Here "best possible" means the type most suited to hold the values. For example, this a pandas integer type, if all of the values are integers (or missing values): an object column of Python integer objects are converted to Int64, a column of NumPy int32 values, will become the pandas dtype Int32.

With our object DataFrame df, we get the following result:

>>> df.convert_dtypes().dtypes                                             
a     Int64
b    string
dtype: object

Since column 'a' held integer values, it was converted to the Int64 type (which is capable of holding missing values, unlike int64).

Column 'b' contained string objects, so was changed to pandas' string dtype.

By default, this method will infer the type from object values in each column. We can change this by passing infer_objects=False:

>>> df.convert_dtypes(infer_objects=False).dtypes                          
a    object
b    string
dtype: object

Now column 'a' remained an object column: pandas knows it can be described as an 'integer' column (internally it ran infer_dtype) but didn't infer exactly what dtype of integer it should have so did not convert it. Column 'b' was again converted to 'string' dtype as it was recognised as holding 'string' values.

Constantia

Answer 2

Use this:

a = [['a', '1.2', '4.2'], ['b', '70', '0.03'], ['x', '5', '0']]
df = pd.DataFrame(a, columns=['one', 'two', 'three'])
df

Out[16]:
  one  two three
0   a  1.2   4.2
1   b   70  0.03
2   x    5     0

df.dtypes

Out[17]:
one      object
two      object
three    object

df[['two', 'three']] = df[['two', 'three']].astype(float)

df.dtypes

Out[19]:
one       object
two      float64
three    float64

Saturable

Answer 3

This below code will change the datatype of a column.

df[['col.name1', 'col.name2'...]] = df[['col.name1', 'col.name2'..]].astype('data_type')

In place of the data type, you can give your datatype what you want, like, str, float, int, etc.

Chamfron

Mind you that when applying this on a column containing the strings ``` 'True' ``` and ``` 'False' ``` using the data_type bool, everything is changed to True. – Captor

Answer 4

When I've only needed to specify specific columns, and I want to be explicit, I've used (per pandas.DataFrame.astype):

dataframe = dataframe.astype({'col_name_1':'int','col_name_2':'float64', etc. ...})

So, using the original question, but providing column names to it...

a = [['a', '1.2', '4.2'], ['b', '70', '0.03'], ['x', '5', '0']]
df = pd.DataFrame(a, columns=['col_name_1', 'col_name_2', 'col_name_3'])
df = df.astype({'col_name_2':'float64', 'col_name_3':'float64'})

Neptunian

Answer 5

pandas >= 1.0

Here's a chart that summarises some of the most important conversions in pandas.

Conversions to string are trivial .astype(str) and are not shown in the figure.

"Hard" versus "Soft" conversions

Note that "conversions" in this context could either refer to converting text data into their actual data type (hard conversion), or inferring more appropriate data types for data in object columns (soft conversion). To illustrate the difference, take a look at

df = pd.DataFrame({'a': ['1', '2', '3'], 'b': [4, 5, 6]}, dtype=object)
df.dtypes

a    object
b    object
dtype: object

# Actually converts string to numeric - hard conversion
df.apply(pd.to_numeric).dtypes

a    int64
b    int64
dtype: object

# Infers better data types for object data - soft conversion
df.infer_objects().dtypes

a    object  # no change
b     int64
dtype: object

# Same as infer_objects, but converts to equivalent ExtensionType
    df.convert_dtypes().dtypes

Umbrage

Answer 6

df = df.astype({"columnname": str})

#e.g - for changing the column type to string #df is your dataframe

Desilva

Answer 7

Here is a function that takes as its arguments a DataFrame and a list of columns and coerces all data in the columns to numbers.

# df is the DataFrame, and column_list is a list of columns as strings (e.g ["col1","col2","col3"])
# dependencies: pandas

def coerce_df_columns_to_numeric(df, column_list):
    df[column_list] = df[column_list].apply(pd.to_numeric, errors='coerce')

So, for your example:

import pandas as pd

def coerce_df_columns_to_numeric(df, column_list):
    df[column_list] = df[column_list].apply(pd.to_numeric, errors='coerce')

a = [['a', '1.2', '4.2'], ['b', '70', '0.03'], ['x', '5', '0']]
df = pd.DataFrame(a, columns=['col1','col2','col3'])

coerce_df_columns_to_numeric(df, ['col2','col3'])

Tobietobin

Answer 8

1. Convert string representation of long floats to numeric values

If a column contains string representation of really long floats that need to be evaluated with precision (float would round them after 15 digits and pd.to_numeric is even more imprecise), then use Decimal from the builtin decimal library. The dtype of the column will be object but decimal.Decimal supports all arithmetic operations, so you can still perform vectorized operations such as arithmetic and comparison operators etc.

from decimal import Decimal
df = pd.DataFrame({'long_float': ["0.1234567890123456789", "0.123456789012345678", "0.1234567890123456781"]})

df['w_float'] = df['long_float'].astype(float)       # imprecise
df['w_Decimal'] = df['long_float'].map(Decimal)      # precise

In the example above, float converts all of them into the same number whereas Decimal maintains their difference:

df['w_Decimal'] == Decimal(df.loc[1, 'long_float'])   # False, True, False
df['w_float'] == float(df.loc[1, 'long_float'])       # True, True, True

2. Convert string representation of long integers to integers

By default, astype(int) converts to int32, which wouldn't work (OverflowError) if a number is particularly long (such as phone number); try 'int64' (or even float) instead:

df['long_num'] = df['long_num'].astype('int64')

On a side note, if you get SettingWithCopyWarning, then turn on copy-on-write mode (see this answer for more info) and do whatever you were doing again. For example, if you were converting col1 and col2 to float dtype, then do:

pd.set_option('mode.copy_on_write', True)
df[['col1', 'col2']] = df[['col1', 'col2']].astype(float)

# or use assign to overwrite the old columns and make a new copy
df = df.assign(**df[['col1', 'col2']].astype(float))

3. Convert integers to timedelta

Also, the long string/integer maybe datetime or timedelta, in which case, use to_datetime or to_timedelta to convert to datetime/timedelta dtype:

df = pd.DataFrame({'long_int': ['1018880886000000000', '1590305014000000000', '1101470895000000000', '1586646272000000000', '1460958607000000000']})
df['datetime'] = pd.to_datetime(df['long_int'].astype('int64'))
# or
df['datetime'] = pd.to_datetime(df['long_int'].astype(float))

df['timedelta'] = pd.to_timedelta(df['long_int'].astype('int64'))

4. Convert timedelta to numbers

To perform the reverse operation (convert datetime/timedelta to numbers), view it as 'int64'. This could be useful if you were building a machine learning model that somehow needs to include time (or datetime) as a numeric value. Just make sure that if the original data are strings, then they must be converted to timedelta or datetime before any conversion to numbers.

df = pd.DataFrame({'Time diff': ['2 days 4:00:00', '3 days', '4 days', '5 days', '6 days']})
df['Time diff in nanoseconds'] = pd.to_timedelta(df['Time diff']).view('int64')
df['Time diff in seconds'] = pd.to_timedelta(df['Time diff']).view('int64') // 10**9
df['Time diff in hours'] = pd.to_timedelta(df['Time diff']).view('int64') // (3600*10**9)

5. Convert datetime to numbers

For datetime, the numeric view of a datetime is the time difference between that datetime and the UNIX epoch (1970-01-01).

df = pd.DataFrame({'Date': ['2002-04-15', '2020-05-24', '2004-11-26', '2020-04-11', '2016-04-18']})
df['Time_since_unix_epoch'] = pd.to_datetime(df['Date'], format='%Y-%m-%d').view('int64')

6. astype is faster than to_numeric

df = pd.DataFrame(np.random.default_rng().choice(1000, size=(10000, 50)).astype(str))
df = pd.concat([df, pd.DataFrame(np.random.rand(10000, 50).astype(str), columns=range(50, 100))], axis=1)

%timeit df.astype(dict.fromkeys(df.columns[:50], int) | dict.fromkeys(df.columns[50:], float))
# 488 ms ± 28 ms per loop (mean ± std. dev. of 7 runs, 10 loops each)

%timeit df.apply(pd.to_numeric)
# 686 ms ± 45.8 ms per loop (mean ± std. dev. of 7 runs, 10 loops each)

Rhapsodist

Great answer. For other readers, be cautious when display the resulting data frame contents that you don't get fooled by the default Pandas numeric display precision which is 6 as seen using pd.options.display.precision. This will make it look like you have less precision than you really do. You can use something like this with pd.option_context('display.float_format', '{:0.20f}'.format): print(df) to see 20 digits. – Nabala

Answer 9

Create two dataframes, each with different data types for their columns, and then appending them together:

d1 = pd.DataFrame(columns=[ 'float_column' ], dtype=float)
d1 = d1.append(pd.DataFrame(columns=[ 'string_column' ], dtype=str))

Results

In[8}:  d1.dtypes
Out[8]:
float_column     float64
string_column     object
dtype: object

After the dataframe is created, you can populate it with floating point variables in the 1st column, and strings (or any data type you desire) in the 2nd column.

Newel

Answer 10

df.info() gives us initial datatype of temp which is float64

 #   Column  Non-Null Count  Dtype  
---  ------  --------------  -----  
 0   date    132 non-null    object 
 1   temp    132 non-null    float64

Now, use this code to change the datatype to int64:

df['temp'] = df['temp'].astype('int64')

if you do df.info() again, you will see:

  #   Column  Non-Null Count  Dtype 
 ---  ------  --------------  ----- 
  0   date    132 non-null    object
  1   temp    132 non-null    int64 

This shows you have successfully changed the datatype of column temp. Happy coding!

Kovno

Answer 11

Starting pandas 1.0.0, we have pandas.DataFrame.convert_dtypes. You can even control what types to convert!

In [40]: df = pd.DataFrame(
    ...:     {
    ...:         "a": pd.Series([1, 2, 3], dtype=np.dtype("int32")),
    ...:         "b": pd.Series(["x", "y", "z"], dtype=np.dtype("O")),
    ...:         "c": pd.Series([True, False, np.nan], dtype=np.dtype("O")),
    ...:         "d": pd.Series(["h", "i", np.nan], dtype=np.dtype("O")),
    ...:         "e": pd.Series([10, np.nan, 20], dtype=np.dtype("float")),
    ...:         "f": pd.Series([np.nan, 100.5, 200], dtype=np.dtype("float")),
    ...:     }
    ...: )

In [41]: dff = df.copy()

In [42]: df 
Out[42]: 
   a  b      c    d     e      f
0  1  x   True    h  10.0    NaN
1  2  y  False    i   NaN  100.5
2  3  z    NaN  NaN  20.0  200.0

In [43]: df.dtypes
Out[43]: 
a      int32
b     object
c     object
d     object
e    float64
f    float64
dtype: object

In [44]: df = df.convert_dtypes()

In [45]: df.dtypes
Out[45]: 
a      Int32
b     string
c    boolean
d     string
e      Int64
f    float64
dtype: object

In [46]: dff = dff.convert_dtypes(convert_boolean = False)

In [47]: dff.dtypes
Out[47]: 
a      Int32
b     string
c     object
d     string
e      Int64
f    float64
dtype: object

Capacitate

Answer 12

In case you have various objects columns like this Dataframe of 74 Objects columns and 2 Int columns where each value have letters representing units:

import pandas as pd 
import numpy as np

dataurl = 'https://raw.githubusercontent.com/RubenGavidia/Pandas_Portfolio.py/main/Wes_Mckinney.py/nutrition.csv'
nutrition = pd.read_csv(dataurl,index_col=[0])
nutrition.head(3)

Output:

    name    serving_size    calories    total_fat    saturated_fat    cholesterol    sodium    choline    folate    folic_acid    ...    fat    saturated_fatty_acids    monounsaturated_fatty_acids    polyunsaturated_fatty_acids    fatty_acids_total_trans    alcohol    ash    caffeine    theobromine    water
0    Cornstarch    100 g    381    0.1g    NaN    0    9.00 mg    0.4 mg    0.00 mcg    0.00 mcg    ...    0.05 g    0.009 g    0.016 g    0.025 g    0.00 mg    0.0 g    0.09 g    0.00 mg    0.00 mg    8.32 g
1    Nuts, pecans    100 g    691    72g    6.2g    0    0.00 mg    40.5 mg    22.00 mcg    0.00 mcg    ...    71.97 g    6.180 g    40.801 g    21.614 g    0.00 mg    0.0 g    1.49 g    0.00 mg    0.00 mg    3.52 g
2    Eggplant, raw    100 g    25    0.2g    NaN    0    2.00 mg    6.9 mg    22.00 mcg    0.00 mcg    ...    0.18 g    0.034 g    0.016 g    0.076 g    0.00 mg    0.0 g    0.66 g    0.00 mg    0.00 mg    92.30 g
3 rows × 76 columns

nutrition.dtypes
name             object
serving_size     object
calories          int64
total_fat        object
saturated_fat    object
                  ...
alcohol          object
ash              object
caffeine         object
theobromine      object
water            object
Length: 76, dtype: object

nutrition.dtypes.value_counts()
object    74
int64      2
dtype: int64

A good way to convert to numeric all columns is using regular expressions to replace the units for nothing and astype(float) for change the columns data type to float:

nutrition.index = pd.RangeIndex(start = 0, stop = 8789, step= 1)
nutrition.set_index('name',inplace = True)
nutrition.replace('[a-zA-Z]','', regex= True, inplace=True)
nutrition=nutrition.astype(float)
nutrition.head(3)

Output:

serving_size    calories    total_fat    saturated_fat    cholesterol    sodium    choline    folate    folic_acid    niacin    ...    fat    saturated_fatty_acids    monounsaturated_fatty_acids    polyunsaturated_fatty_acids    fatty_acids_total_trans    alcohol    ash    caffeine    theobromine    water
name
Cornstarch    100.0    381.0    0.1    NaN    0.0    9.0    0.4    0.0    0.0    0.000    ...    0.05    0.009    0.016    0.025    0.0    0.0    0.09    0.0    0.0    8.32
Nuts, pecans    100.0    691.0    72.0    6.2    0.0    0.0    40.5    22.0    0.0    1.167    ...    71.97    6.180    40.801    21.614    0.0    0.0    1.49    0.0    0.0    3.52
Eggplant, raw    100.0    25.0    0.2    NaN    0.0    2.0    6.9    22.0    0.0    0.649    ...    0.18    0.034    0.016    0.076    0.0    0.0    0.66    0.0    0.0    92.30
3 rows × 75 columns

nutrition.dtypes
serving_size     float64
calories         float64
total_fat        float64
saturated_fat    float64
cholesterol      float64
                  ...
alcohol          float64
ash              float64
caffeine         float64
theobromine      float64
water            float64
Length: 75, dtype: object

nutrition.dtypes.value_counts()
float64    75
dtype: int64

Now the dataset is clean and you are able to do numeric operations with this Dataframe only with regex and astype().

If you want to collect the units and paste on the headers like cholesterol_mg you can use this code:

nutrition.index = pd.RangeIndex(start = 0, stop = 8789, step= 1)
nutrition.set_index('name',inplace = True)
nutrition.astype(str).replace('[^a-zA-Z]','', regex= True)
units = nutrition.astype(str).replace('[^a-zA-Z]','', regex= True)
units = units.mode()
units = units.replace('', np.nan).dropna(axis=1)
mapper = { k: k + "_" + units[k].at[0] for k in units}
nutrition.rename(columns=mapper, inplace=True)
nutrition.replace('[a-zA-Z]','', regex= True, inplace=True)
nutrition=nutrition.astype(float)

Ethben

Answer 13

I had the same issue.

I could not find any solution that was satisfying. My solution was simply to convert those float into str and remove the '.0' this way.

In my case, I just apply it on the first column:

firstCol = list(df.columns)[0]
df[firstCol] = df[firstCol].fillna('').astype(str).apply(lambda x: x.replace('.0', ''))

Carton

Answer 14

Is there a way to specify the types while converting to DataFrame?

Yes. The other answers convert the dtypes after creating the DataFrame, but we can specify the types at creation. Use either DataFrame.from_records or read_csv(dtype=...) depending on the input format.

The latter is sometimes necessary to avoid memory errors with big data.

---

1. DataFrame.from_records

Create the DataFrame from a structured array of the desired column types:

x = [['foo', '1.2', '70'], ['bar', '4.2', '5']]

df = pd.DataFrame.from_records(np.array(
    [tuple(row) for row in x], # pass a list-of-tuples (x can be a list-of-lists or 2D array)
    'object, float, int'       # define the column types
))

Output:

>>> df.dtypes
# f0     object
# f1    float64
# f2      int64
# dtype: object

---

2. read_csv(dtype=...)

If you're reading the data from a file, use the dtype parameter of read_csv to set the column types at load time.

For example, here we read 30M rows with rating as 8-bit integers and genre as categorical:

lines = '''
foo,biography,5
bar,crime,4
baz,fantasy,3
qux,history,2
quux,horror,1
'''
columns = ['name', 'genre', 'rating']
csv = io.StringIO(lines * 6_000_000) # 30M lines

df = pd.read_csv(csv, names=columns, dtype={'rating': 'int8', 'genre': 'category'})

In this case, we halve the memory usage upon load:

>>> df.info(memory_usage='deep')
# memory usage: 1.8 GB

>>> pd.read_csv(io.StringIO(lines * 6_000_000)).info(memory_usage='deep')
# memory usage: 3.7 GB

This is one way to avoid memory errors with big data. It's not always possible to change the dtypes after loading since we might not have enough memory to load the default-typed data in the first place.

Kerrykersey

Answer 15

I thought I had the same problem, but actually I have a slight difference that makes the problem easier to solve. For others looking at this question, it's worth checking the format of your input list. In my case the numbers are initially floats, not strings as in the question:

a = [['a', 1.2, 4.2], ['b', 70, 0.03], ['x', 5, 0]]

But by processing the list too much before creating the dataframe, I lose the types and everything becomes a string.

Creating the data frame via a NumPy array:

df = pd.DataFrame(np.array(a))
df

Out[5]:
   0    1     2
0  a  1.2   4.2
1  b   70  0.03
2  x    5     0

df[1].dtype
Out[7]: dtype('O')

gives the same data frame as in the question, where the entries in columns 1 and 2 are considered as strings. However doing

df = pd.DataFrame(a)

df
Out[10]:
   0     1     2
0  a   1.2  4.20
1  b  70.0  0.03
2  x   5.0  0.00

df[1].dtype
Out[11]: dtype('float64')

does actually give a data frame with the columns in the correct format.

Beaverette

Answer 16

If you want convert one column from string format I suggest use this code"

import pandas as pd
#My Test Data
data = {'Product': ['A','B', 'C','D'],
          'Price': ['210','250', '320','280']}
data


#Create Data Frame from My data df = pd.DataFrame(data)

#Convert to number
df['Price'] = pd.to_numeric(df['Price'])
df

Total = sum(df['Price'])
Total

else if you going to convert a number of column values to number I suggest to you first filter your values and save in empty array and after that convert to number. I hope this code solve your problem.

Perutz

Answer 17

You can achieve this by using the pd.to_numeric function after creating the DataFrame. Here's how you can convert columns 2 and 3 to floats in your DataFrame:

import pandas as pd

table = [
    ['a', '1.2', '4.2'],
    ['b', '70', '0.03'],
    ['x', '5', '0'],
]

df = pd.DataFrame(table)

# Convert columns 2 and 3 to floats
df.iloc[:, 1:] = df.iloc[:, 1:].apply(pd.to_numeric, errors='coerce')

# Now, df contains the desired types for columns 2 and 3
print(df.dtypes)

This approach uses the pd.to_numeric function with the apply method on the desired columns. The errors='coerce' parameter ensures that any non-numeric values are converted to NaN.

This way, you don't need to specify the column types beforehand, and it will dynamically convert columns to the appropriate type based on their content.

Cabalist