I am using the following code to convert my pandas into sql, but I get the following error although my dtype is float64 for this particular column.
I have tried to convert my dtype to str, but this did not work.
import sqlite3
import pandas as pd
#create db file
db = conn = sqlite3.connect(‘example.db’)
#convert my df data to sql
df = df(‘users’ , con=db, if_exists='replace')
InterfaceError: Error binding parameter 1214 - probably unsupported type.
However when I check the parameter 1214 i.e. column 1214 in my df. This col has a float64 dtype. I don't understand then how to solve this problem.
Double check your data types, as SQLite supports a limited number of data types --> https://www.sqlite.org/datatype3.html. My guess would be to use a float dtype (so try dtype='float')
Related
My csv file contains numeric data where some values have greater than or less than symbols e.g. ">244". I want my data type to be a float. When reading the file into pandas:
df = pd.read_csv('file.csv')
I get a warning:
Columns (2) have mixed types. Specify dtype option on import or set low_memory=False.
I have checked this question: Pandas read_csv: low_memory and dtype options and tried specifying the date type of the relevant column with:
df = pd.read_csv('file.csv',dtype={'column':'float'})
However, this gives an error:
ValueError: could not convert string to float: '>244'
I have also tried
df = pd.read_csv('file.csv',dtype={'column':'float'}, error_bad_lines=False)
However this does not solve my problem, and I get the same error above.
My problem appears to be that my data has a mixture of string and floats. Can I ignore any rows containing strings in particular columns when reading in the data?
You can use:
df = pd.read_csv('file.csv', dtype={'column':'str'})
Then:
df['column'] = pd.to_numeric(df['column'], errors='coerce')
I found a workaround which was read in my data
df = pd.read_csv('file.csv')
Then remove any values with '<' or '>'
df = df.loc[df['column'].str[:1] != '<']
df = df.loc[df['column'].str[:1] != '>']
Then convert to numeric with pd.to_numeric
df['column'] = pd.to_numeric(df['column'])
I want to get some data in a dictionary that need to go into a pandas dataframe.
The dataframe is later written in a PostgreSQL table using sqlalchemy, and I would like to get the right column types.
Hence, I specify the dtypes for the dataframe
dtypes = {"forretningshændelse": sqlalchemy.types.String(length=8),
"forretningsområde": sqlalchemy.types.String(length=40),
"forretningsproces": sqlalchemy.types.INTEGER(),
"id_namespace": sqlalchemy.types.String(length=100),
"id_lokalId": sqlalchemy.types.String(length=36),
"kommunekode": sqlalchemy.types.INTEGER(),
"registreringFra": sqlalchemy.types.DateTime()}
Later I use df = pd.DataFrame(item_lst, dtype=dtypes), where item_lst is a list of dictionaries.
Independent from me using either String(8), String(length=8) or VARCHAR(8) in the dtype definition, the result of pd.DataFrame(item_lst, dtype=dtypes) is always object of type '(String or VARCHAR)' has no len().
How do I have to define the dtype to overcome this error?
Instead of forcing data types when the DataFrame is created, let pandas infer the data types (just df = pd.DataFrame(item_lst)) and then use your dtypes dict with to_sql() when you push your DataFrame to the database, like this:
from pprint import pprint
import pandas as pd
import sqlalchemy
engine = sqlalchemy.create_engine("sqlite://")
item_lst = [{"forretningshændelse": "foo"}]
df = pd.DataFrame(item_lst)
print(df.info())
"""
<class 'pandas.core.frame.DataFrame'>
RangeIndex: 1 entries, 0 to 0
Data columns (total 1 columns):
# Column Non-Null Count Dtype
--- ------ -------------- -----
0 forretningshændelse 1 non-null object
dtypes: object(1)
memory usage: 136.0+ bytes
None
"""
dtypes = {"forretningshændelse": sqlalchemy.types.String(length=8)}
df.to_sql("tbl", engine, index=False, dtype=dtypes)
insp = sqlalchemy.inspect(engine)
pprint(insp.get_columns("tbl"))
"""
[{'autoincrement': 'auto',
'default': None,
'name': 'forretningshændelse',
'nullable': True,
'primary_key': 0,
'type': VARCHAR(length=8)}]
"""
I believe you are confusing the dtypes within the DataFrame with the dtypes on the SQL table itself.
You probably don't need to manually specify the datatypes in pandas itself but if you do, here's how.
Spoiler alert: it is written in the pandas.Dataframe documentation that only a single dtype must be specified so you will need some loops or manual column work to get different types.
To solve your problem:
import pandas as pd
import sqlalchemy
engine = sqlalchemy.create_engine("connection_string")
df = pd.DataFrame(item_list)
dtypes = {"forretningshændelse": sqlalchemy.types.String(length=8),
"forretningsområde": sqlalchemy.types.String(40),
"forretningsproces": sqlalchemy.types.INTEGER(),
"id_namespace": sqlalchemy.types.String(100),
"id_lokalId": sqlalchemy.types.String(36),
"kommunekode": sqlalchemy.types.INTEGER(),
"registreringFra": sqlalchemy.types.DateTime()}
with engine.connect() as engine:
df.to_sql("table_name",if_exists="replace", con=engine, dtype=dtypes)
Tip: Avoid using special characters while coding in general, it only makes maintaining code harder at some point :). I assumed you're creating a new sql table and not appending, otherwise types for the table would already be defined.
Happy Coding!
I have a DataFrame from pandas:
i want to get a mean value of "stop_duration" for each "violation_raw".
How can i do it if column "stop_duration" is object type
df = enter code herepd.read_csv('police.csv', parse_dates=['stop_date'])
df[['stop_date', 'violation_raw','stop_duration']]
My table:
the table
Use to_datetime function to convert object to datetime. Also specifying a format to match your data.
import pandas as pd
df["column"] = pd.to_datetime(df["column"], format="%M-%S Min")
I’m trying to read a CSV into Pandas, and then write it to Parquet. The challenge is that the CSV has a date column with a value of 3000-12-31, and apparently Pandas has no way to store that value as an actual date. Because of that, PyArrow fails to read the date value.
An example file and code to reproduce is
test.csv
t
3000-12-31
import pandas as pd
import pyarrow as pa
df = pd.read_csv("test.csv", parse_dates=["t"])
schema = pa.schema([pa.field("t", pa.date64())])
table = pa.Table.from_pandas(df, schema=schema)
This gives (a somewhat unhelpful error)
TypeError: an integer is required (got type str)
What's the right way to do this?
Pandas datetime columns (which use the datetime64[ns] data type) indeed cannot store such dates.
One possible workaround to convert the strings to datetime.datetime objects in an object dtype column. And then pyarrow should be able to accept them to create a date column.
This conversion could eg be done with dateutil:
>>> import dateutil
>>> df['t'] = df['t'].apply(dateutil.parser.parse)
>>> df
t
0 3000-12-31 00:00:00
>>> table = pa.Table.from_pandas(df, schema=schema)
>>> table
pyarrow.Table
t: date64[ms]
or if you use a fixed format, using datetime.date.strptime is probably more reliable:
>>> import datetime
>>> df['t'] = df['t'].apply(lambda s: datetime.datetime.strptime(s, "%Y-%m-%d"))
>>> table = pa.Table.from_pandas(df, schema=schema)
>>> table
pyarrow.Table
t: date64[ms]
I have a data frame df and there I want to convert some columns into category type. Using pandas I can do it like below way:
for col in categorical_collist:
df[col] = df[col].astype('category')
I want to do the column conversion in pyspark. How can I do it?
I have tried using the below code in pyspark. But it is not giving my expected output during operation.
from pyspark.sql.types import StringType
for col in categorical_collist:
df = df.withColumn(col, df[col].cast(StringType()))