DataFrame.categorize ([columns, index, …]) Convert columns of the DataFrame to category dtype. DataFrame.astype (dtype) Cast a pandas object to a specified dtype dtype. Add new columns in a dataFrame using Dataframe.assign() Pandas Library provides a function to add columns i.e.

drop ( 'name' , axis = 1 ) # Return the square root of every cell in the dataframe df . DataFrame.astype (self, dtype, copy, errors). Attempt to infer better dtypes for object columns. 1. assign (* * kwargs) It accepts a keyword & value pairs, where a keyword is column name and value is either list / series or a callable entry. pandas.core.groupby.GroupBy.apply¶ GroupBy.apply (self, func, *args, **kwargs) [source] ¶ Apply function func group-wise and combine the results together.. I'd like to apply a function with multiple returns to a pandas DataFrame and put the results in separate new columns in that DataFrame. To see how to apply this template in practice, I’ll review two cases of: Adding a single column to an existing DataFrame; and; Adding multiple columns to a DataFrame; Case 1: Add Single Column to Pandas DataFrame using Assign.
Cast a pandas object to a specified dtype dtype.. DataFrame.convert_dtypes (self, …). DataFrame.columns The scenario is this: we have a DataFrame of a moderate size, say 1 million rows and a dozen columns. Python is a great language for doing data analysis, primarily because of the fantastic ecosystem of data-centric python packages. Apply a square root function to every single cell in the whole data frame applymap() applies a function to every single element in the entire dataframe. DataFrame.apply (func[, axis, broadcast, …]) Parallel version of pandas.DataFrame.apply: DataFrame.assign (**kwargs) Assign new columns to a DataFrame.

The function passed to apply must take a dataframe as its first argument and return a DataFrame, Series or scalar.apply will then take care of combining the results back together into a single dataframe or series. Your Python content is far superior than many others.
# Drop the string variable so that applymap() can run df = df . So given something like this: ... One way to do this would be to use pd.DataFrame.assign as follows: Method 1 : Using Dataframe.apply() ... # Apply a function to one row and assign it back to the row in dataframe. I have the following data frame in IPython, where each row is a single stock: In [261]: bdata Out[261]: Int64Index: 21210 entries, 0 to 21209 Data columns: BloombergTicker 21206 non-null values Company 21210 non-null values Country 21210 non-null values MarketCap 21210 non-null values PriceReturn 21210 non-null values SEDOL 21210 non-null values … As to your second question, the function my_test(a) doesn't know what df is since it wasn't passed in as an argument (unless df is supposed to be a global, which would be terrible practice). Python.