I am importing study data into a Pandas data frame using read_csv.
My subject codes are 6 numbers coding, among others, the day of birth. For some of my subjects this results in a code with a leading zero (e.g. "010816").
When I import into Pandas, the leading zero is stripped of and the column is formatted as int64.
Is there a way to import this column unchanged maybe as a string?
I tried using a custom converter for the column, but it does not work - it seems as if the custom conversion takes place before Pandas converts to int.
As indicated in this answer by Lev Landau, there could be a simple solution to use converters option for a certain column in read_csv function.
converters={'column_name': str}
Let's say I have csv file projects.csv like below:
project_name,project_id
Some Project,000245
Another Project,000478
As for example below code is trimming leading zeros:
from pandas import read_csv
dataframe = read_csv('projects.csv')
print dataframe
Result:
project_name project_id
0 Some Project 245
1 Another Project 478
Solution code example:
from pandas import read_csv
dataframe = read_csv('projects.csv', converters={'project_id': str})
print dataframe
Required result:
project_name project_id
0 Some Project 000245
1 Another Project 000478
To have all columns as str:
pd.read_csv('sample.csv', dtype=str)
To have certain columns as str:
# column names which need to be string
lst_str_cols = ['prefix', 'serial']
dict_dtypes = {x: 'str' for x in lst_str_cols}
pd.read_csv('sample.csv', dtype=dict_dtypes)
here is a shorter, robust and fully working solution:
simply define a mapping (dictionary) between variable names and desired data type:
dtype_dic= {'subject_id': str,
'subject_number' : 'float'}
use that mapping with pd.read_csv():
df = pd.read_csv(yourdata, dtype = dtype_dic)
et voila!
If you have a lot of columns and you don't know which ones contain leading zeros that might be missed, or you might just need to automate your code. You can do the following:
df = pd.read_csv("your_file.csv", nrows=1) # Just take the first row to extract the columns' names
col_str_dic = {column:str for column in list(df)}
df = pd.read_csv("your_file.csv", dtype=col_str_dic) # Now you can read the compete file
You could also do:
df = pd.read_csv("your_file.csv", dtype=str)
By doing this you will have all your columns as strings and you won't lose any leading zeros.
You Can do This , Works On all Versions of Pandas
pd.read_csv('filename.csv', dtype={'zero_column_name': object})
You can use converters to convert number to fixed width if you know the width.
For example, if the width is 5, then
data = pd.read_csv('text.csv', converters={'column1': lambda x: f"{x:05}"})
This will do the trick. It works for pandas==0.23.0 and also read_excel.
Python3.6 or higher required.
I don't think you can specify a column type the way you want (if there haven't been changes reciently and if the 6 digit number is not a date that you can convert to datetime). You could try using np.genfromtxt() and create the DataFrame from there.
EDIT: Take a look at Wes Mckinney's blog, there might be something for you. It seems to be that there is a new parser from pandas 0.10 coming in November.
As an example, consider the following my_data.txt file:
id,A
03,5
04,6
To preserve the leading zeros for the id column:
df = pd.read_csv("my_data.txt", dtype={"id":"string"})
df
id A
0 03 5
1 04 6
I'm running into a strange issue where combine_first method is causing values stored as bool to be upcasted into float64s.
Example:
In [1]: import pandas as pd
In [2]: df1 = pd.DataFrame({"a": [True]})
In [3]: df2 = pd.DataFrame({"b": ['test']})
In [4]: df2.combine_first(df1)
Out[4]:
a b
0 1.0 test
This problem has already been reported in a previous post 3 years ago: pandas DataFrame combine_first and update methods have strange behavior. This issue was told to be solved but I still have this behaviour under pandas 0.18.1
thank you for your help
Somewhere along the chain of events to get to a combined dataframe, potential missing values had to be addressed. I'm aware that nothing is missing in your example. None and np.nan are not int, or bool. So in order to have a common dtype that contains a bool and a None or np.nan it is necessary to cast the column as either object or float. As 'float`, a large number of operations become far more efficient and is a decent choice. It obviously isn't the best choice all of the time, but a choice has to be made none the less and pandas tried to infer the best one.
A work around:
Setup
df1 = pd.DataFrame({"a": [True]})
df2 = pd.DataFrame({"b": ['test']})
df3 = df2.combine_first(df1)
df3
Solution
dtypes = df1.dtypes.combine_first(df2.dtypes)
for k, v in dtypes.iteritems():
df3[k] = df3[k].astype(v)
df3
I ran into the same issue. This specific case does not seem to be fixed in Pandas yet. I've filed a bug report:
https://github.com/pandas-dev/pandas/issues/20699
I read data from a .csv file to a Pandas dataframe as below. For one of the columns, namely id, I want to specify the column type as int. The problem is the id series has missing/empty values.
When I try to cast the id column to integer while reading the .csv, I get:
df= pd.read_csv("data.csv", dtype={'id': int})
error: Integer column has NA values
Alternatively, I tried to convert the column type after reading as below, but this time I get:
df= pd.read_csv("data.csv")
df[['id']] = df[['id']].astype(int)
error: Cannot convert NA to integer
How can I tackle this?
In version 0.24.+ pandas has gained the ability to hold integer dtypes with missing values.
Nullable Integer Data Type.
Pandas can represent integer data with possibly missing values using arrays.IntegerArray. This is an extension types implemented within pandas. It is not the default dtype for integers, and will not be inferred; you must explicitly pass the dtype into array() or Series:
arr = pd.array([1, 2, np.nan], dtype=pd.Int64Dtype())
pd.Series(arr)
0 1
1 2
2 NaN
dtype: Int64
For convert column to nullable integers use:
df['myCol'] = df['myCol'].astype('Int64')
The lack of NaN rep in integer columns is a pandas "gotcha".
The usual workaround is to simply use floats.
My use case is munging data prior to loading into a DB table:
df[col] = df[col].fillna(-1)
df[col] = df[col].astype(int)
df[col] = df[col].astype(str)
df[col] = df[col].replace('-1', np.nan)
Remove NaNs, convert to int, convert to str and then reinsert NANs.
It's not pretty but it gets the job done!
It is now possible to create a pandas column containing NaNs as dtype int, since it is now officially added on pandas 0.24.0
pandas 0.24.x release notes
Quote: "Pandas has gained the ability to hold integer dtypes with missing values
Whether your pandas series is object datatype or simply float datatype the below method will work
df = pd.read_csv("data.csv")
df['id'] = df['id'].astype(float).astype('Int64')
I had the problem a few weeks ago with a few discrete features which were formatted as 'object'. This solution seemed to work.
for col in discrete:
df[col] = pd.to_numeric(df[col],errors='coerce').astype(pd.Int64Dtype())
If you absolutely want to combine integers and NaNs in a column, you can use the 'object' data type:
df['col'] = (
df['col'].fillna(0)
.astype(int)
.astype(object)
.where(df['col'].notnull())
)
This will replace NaNs with an integer (doesn't matter which), convert to int, convert to object and finally reinsert NaNs.
You could use .dropna() if it is OK to drop the rows with the NaN values.
df = df.dropna(subset=['id'])
Alternatively,
use .fillna() and .astype() to replace the NaN with values and convert them to int.
I ran into this problem when processing a CSV file with large integers, while some of them were missing (NaN). Using float as the type was not an option, because I might loose the precision.
My solution was to use str as the intermediate type.
Then you can convert the string to int as you please later in the code. I replaced NaN with 0, but you could choose any value.
df = pd.read_csv(filename, dtype={'id':str})
df["id"] = df["id"].fillna("0").astype(int)
For the illustration, here is an example how floats may loose the precision:
s = "12345678901234567890"
f = float(s)
i = int(f)
i2 = int(s)
print (f, i, i2)
And the output is:
1.2345678901234567e+19 12345678901234567168 12345678901234567890
As of Pandas 1.0.0 you can now use pandas.NA values. This does not force integer columns with missing values to be floats.
When reading in your data all you have to do is:
df= pd.read_csv("data.csv", dtype={'id': 'Int64'})
Notice the 'Int64' is surrounded by quotes and the I is capitalized. This distinguishes Panda's 'Int64' from numpy's int64.
As a side note, this will also work with .astype()
df['id'] = df['id'].astype('Int64')
Documentation here
https://pandas.pydata.org/pandas-docs/stable/user_guide/integer_na.html
If you can modify your stored data, use a sentinel value for missing id. A common use case, inferred by the column name, being that id is an integer, strictly greater than zero, you could use 0 as a sentinel value so that you can write
if row['id']:
regular_process(row)
else:
special_process(row)
Most solutions here tell you how to use a placeholder integer to represent nulls. That approach isn't helpful if you're uncertain that integer won't show up in your source data though. My method with will format floats without their decimal values and convert nulls to None's. The result is an object datatype that will look like an integer field with null values when loaded into a CSV.
keep_df[col] = keep_df[col].apply(lambda x: None if pandas.isnull(x) else '{0:.0f}'.format(pandas.to_numeric(x)))
import pandas as pd
df= pd.read_csv("data.csv")
df['id'] = pd.to_numeric(df['id'])
If you want to use it when you chain methods, you can use assign:
df = (
df.assign(col = lambda x: x['col'].astype('Int64'))
)
The issue with Int64, like many other's solutions, is that if you have null values, they get replaced with <NA> values, which do not work with pandas default 'NaN' functions, like isnull() or fillna(). Or if you convert values to -1 you end up in a situation where you may be deleting your information. My solution is a little lame, but will provide int values with np.nan, allowing for nan functions to work without compromising your values.
def to_int(x):
try:
return int(x)
except:
return np.nan
df[column] = df[column].apply(to_int)
Use .fillna() to replace all NaN values with 0 and then convert it to int using astype(int)
df['id'] = df['id'].fillna(0).astype(int)
For anyone needing to have int values within NULL/NaN-containing columns, but working under the constraint of being unable to use pandas version 0.24.0 nullable integer features mentioned in other answers, I suggest converting the columns to object type using pd.where:
df = df.where(pd.notnull(df), None)
This converts all NaNs in the dataframe to None, treating mixed-type columns as objects, but leaving the int values as int, rather than float.
First you need to specify the newer integer type, Int8 (...Int64) that can handle null integer data (pandas version >= 0.24.0)
df = df.astype('Int8')
But you may want to only target specific columns which have integer data mixed with NaN/nulls:
df = df.astype({'col1':'Int8','col2':'Int8','col3':'Int8')
At this point, the NaN's are converted into <NA> and if you want to change the default null value with df.fillna(), you need to coerce the object datatype on the columns you wish to change, otherwise you will see
TypeError: <U1 cannot be converted to an IntegerDtype
You can do this by
df = df.astype(object) if you don't mind changing every column datatype to object (individually, each value's type is still preserved) ... OR
df = df.astype({"col1": object,"col2": object}) if you prefer to target individual columns.
This should help with forcing your integer columns mixed with nulls to stay formatted as integers and change the null values to whatever you like. I can't speak to the efficiency of this method, but it worked for my formatting and printing purposes.
I ran into this issue working with pyspark. As this is a python frontend for code running on a jvm, it requires type safety and using float instead of int is not an option. I worked around the issue by wrapping the pandas pd.read_csv in a function that will fill user-defined columns with user-defined fill values before casting them to the required type. Here is what I ended up using:
def custom_read_csv(file_path, custom_dtype = None, fill_values = None, **kwargs):
if custom_dtype is None:
return pd.read_csv(file_path, **kwargs)
else:
assert 'dtype' not in kwargs.keys()
df = pd.read_csv(file_path, dtype = {}, **kwargs)
for col, typ in custom_dtype.items():
if fill_values is None or col not in fill_values.keys():
fill_val = -1
else:
fill_val = fill_values[col]
df[col] = df[col].fillna(fill_val).astype(typ)
return df
Try this:
df[['id']] = df[['id']].astype(pd.Int64Dtype())
If you print it's dtypes, you will get id Int64 instead of normal one int64
First remove the rows which contain NaN. Then do Integer conversion on remaining rows.
At Last insert the removed rows again.
Hope it will work
Had a similar problem. That was my solution:
def toint(zahl = 1.1):
try:
zahl = int(zahl)
except:
zahl = np.nan
return zahl
print(toint(4.776655), toint(np.nan), toint('test'))
4 nan nan
df = pd.read_csv("data.csv")
df['id'] = df['id'].astype(float)
df['id'] = toint(df['id'])
Since I didn't see the answer here, I might as well add it:
One-liner to convert NANs to empty string if you for some reason you still can't handle np.na or pd.NA like me when relying on a library with an older version of pandas:
df.select_dtypes('number').fillna(-1).astype(str).replace('-1', '')
I think the approach of #Digestible1010101 is the more appropriate for Pandas 1.2.+ versions, something like this should do the job:
df = df.astype({
'col_1': 'Int64',
'col_2': 'Int64',
'col_3': 'Int64',
'col_4': 'Int64', })
Similar to #hibernado's answer, but keeping it as integers (instead of strings)
df[col] = df[col].fillna(-1)
df[col] = df[col].astype(int)
df[col] = np.where(df[col] == -1, np.nan, df[col])
df.loc[~df['id'].isna(), 'id'] = df.loc[~df['id'].isna(), 'id'].astype('int')
Assuming your DateColumn formatted 3312018.0 should be converted to 03/31/2018 as a string. And, some records are missing or 0.
df['DateColumn'] = df['DateColumn'].astype(int)
df['DateColumn'] = df['DateColumn'].astype(str)
df['DateColumn'] = df['DateColumn'].apply(lambda x: x.zfill(8))
df.loc[df['DateColumn'] == '00000000','DateColumn'] = '01011980'
df['DateColumn'] = pd.to_datetime(df['DateColumn'], format="%m%d%Y")
df['DateColumn'] = df['DateColumn'].apply(lambda x: x.strftime('%m/%d/%Y'))
use pd.to_numeric()
df["DateColumn"] = pd.to_numeric(df["DateColumn"])
simple and clean
I am importing study data into a Pandas data frame using read_csv.
My subject codes are 6 numbers coding, among others, the day of birth. For some of my subjects this results in a code with a leading zero (e.g. "010816").
When I import into Pandas, the leading zero is stripped of and the column is formatted as int64.
Is there a way to import this column unchanged maybe as a string?
I tried using a custom converter for the column, but it does not work - it seems as if the custom conversion takes place before Pandas converts to int.
As indicated in this answer by Lev Landau, there could be a simple solution to use converters option for a certain column in read_csv function.
converters={'column_name': str}
Let's say I have csv file projects.csv like below:
project_name,project_id
Some Project,000245
Another Project,000478
As for example below code is trimming leading zeros:
from pandas import read_csv
dataframe = read_csv('projects.csv')
print dataframe
Result:
project_name project_id
0 Some Project 245
1 Another Project 478
Solution code example:
from pandas import read_csv
dataframe = read_csv('projects.csv', converters={'project_id': str})
print dataframe
Required result:
project_name project_id
0 Some Project 000245
1 Another Project 000478
To have all columns as str:
pd.read_csv('sample.csv', dtype=str)
To have certain columns as str:
# column names which need to be string
lst_str_cols = ['prefix', 'serial']
dict_dtypes = {x: 'str' for x in lst_str_cols}
pd.read_csv('sample.csv', dtype=dict_dtypes)
here is a shorter, robust and fully working solution:
simply define a mapping (dictionary) between variable names and desired data type:
dtype_dic= {'subject_id': str,
'subject_number' : 'float'}
use that mapping with pd.read_csv():
df = pd.read_csv(yourdata, dtype = dtype_dic)
et voila!
If you have a lot of columns and you don't know which ones contain leading zeros that might be missed, or you might just need to automate your code. You can do the following:
df = pd.read_csv("your_file.csv", nrows=1) # Just take the first row to extract the columns' names
col_str_dic = {column:str for column in list(df)}
df = pd.read_csv("your_file.csv", dtype=col_str_dic) # Now you can read the compete file
You could also do:
df = pd.read_csv("your_file.csv", dtype=str)
By doing this you will have all your columns as strings and you won't lose any leading zeros.
You Can do This , Works On all Versions of Pandas
pd.read_csv('filename.csv', dtype={'zero_column_name': object})
You can use converters to convert number to fixed width if you know the width.
For example, if the width is 5, then
data = pd.read_csv('text.csv', converters={'column1': lambda x: f"{x:05}"})
This will do the trick. It works for pandas==0.23.0 and also read_excel.
Python3.6 or higher required.
I don't think you can specify a column type the way you want (if there haven't been changes reciently and if the 6 digit number is not a date that you can convert to datetime). You could try using np.genfromtxt() and create the DataFrame from there.
EDIT: Take a look at Wes Mckinney's blog, there might be something for you. It seems to be that there is a new parser from pandas 0.10 coming in November.
As an example, consider the following my_data.txt file:
id,A
03,5
04,6
To preserve the leading zeros for the id column:
df = pd.read_csv("my_data.txt", dtype={"id":"string"})
df
id A
0 03 5
1 04 6