pandas - w3toppers.com

pyodbc.connect() works, but not sqlalchemy.create_engine().connect()

A Pass through exact Pyodbc string works for me: import pandas as pd from sqlalchemy import create_engine from sqlalchemy.engine import URL connection_string = ( r”Driver=ODBC Driver 17 for SQL Server;” r”Server=(local)\SQLEXPRESS;” r”Database=myDb;” r”Trusted_Connection=yes;” ) connection_url = URL.create( “mssql+pyodbc”, query={“odbc_connect”: connection_string} ) engine = create_engine(connection_url) df = pd.DataFrame([(1, “foo”)], columns=[“id”, “txt”]) pd.to_sql(“test_table”, engine, if_exists=”replace”, index=False)

Numpy.dtype has the wrong size, try recompiling

I’ve seen this error before and it typically does have to do with pandas referencing an old version of numpy. But reinstalling may not help if your python path is still pointing to an old version of numpy. When you install numpy via pip, pip will tell you where it was installed. Something like pip … Read more

Difference between df[x], df[[x]], df[‘x’] , df[[‘x’]] and df.x

df[x] — index a column using variable x. Returns pd.Series df[[x]] — index/slice a single-column DataFrame using variable x. Returns pd.DataFrame df[‘x’] — index a column named ‘x’. Returns pd.Series df[[‘x’]] — index/slice a single-column DataFrame having only one column named ‘x’. Returns pd.DataFrame df.x — dot accessor notation, equivalent to df[‘x’] (there are, however, … Read more

how to understand closed and label arguments in pandas resample method?

Short answer: If you use closed=’left’ and loffset=”2T” then you’ll get what you expected: series.resample(‘3T’, label=”left”, closed=’left’, loffset=”2T”).sum() 2000-01-01 00:02:00 3 2000-01-01 00:05:00 12 2000-01-01 00:08:00 21 Long answer: (or why the results you got were correct, given the arguments you used) This may not be clear from the documentation, but open and closed in … Read more

Pandas min() of selected row and columns

This is a one-liner, you just need to use the axis argument for min to tell it to work across the columns rather than down: df[‘Minimum’] = df.loc[:, [‘B0’, ‘B1’, ‘B2’]].min(axis=1) If you need to use this solution for different numbers of columns, you can use a for loop or list comprehension to construct the … Read more

Remove time portion of DateTime index in pandas

With the date attribute: df.index = df.index.date Example: >>> df = pd.DataFrame([1, 2, 3, 4], index=pd.date_range(‘2018′, periods=4, freq=’H’)) >>> df.index = df.index.date >>> df 0 2018-01-01 1 2018-01-01 2 2018-01-01 3 2018-01-01 4 Note: that this will get you object dtype in Pandas. All attributes are here. It’s technically an array of native Python datetime.date … Read more

Does Pandas calculate ewm wrong?

There are several ways to initialize an exponential moving average, so I wouldn’t say pandas is doing it wrong, just different. Here would be a way to calculate it like you want: In [20]: s.head() Out[20]: 0 22.27 1 22.19 2 22.08 3 22.17 4 22.18 Name: Price, dtype: float64 In [21]: span = 10 … Read more