https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.le.html
pandas.DataFrame.le. #. Get Less than or equal to of dataframe and other, element-wise (binary operator le ). Among flexible wrappers ( eq, ne, le, lt, ge, gt) to comparison operators. Equivalent to ==, !=, <=, <, >=, > with support to choose axis (rows or columns) and level for comparison. Any single or multiple element data structure, or list

https://www.youtube.com/watch?v=P3JPvTVP4aA
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https://www.slingacademy.com/article/pandas-dataframe-lt-and-le-methods-explained-with-examples/
The 'lt ()' and 'le ()' functions in Pandas stand for 'less than' and 'less than or equal to', respectively. These functions are utilized for performing element-wise comparisons either between two DataFrame or Series objects or between a DataFrame or Series and a scalar value. The output is a DataFrame or Series of boolean

https://www.w3schools.com/python/pandas/ref_df_le.asp
Pandas DataFrame le() Method DataFrame Reference. Example. Find which values are less than, or equal to 7: import pandas as pd df = pd.DataFrame([[10, 12, 2], [3, 4, 7]]) print(df.le(7)) ... dataframe.le(other, axis, level) Parameters. Parameter Description; other: Required. A number, list of numbers, or another object with a data structure

https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html
class pandas.DataFrame(data=None, index=None, columns=None, dtype=None, copy=None) [source] #. Two-dimensional, size-mutable, potentially heterogeneous tabular data. Data structure also contains labeled axes (rows and columns). Arithmetic operations align on both row and column labels. Can be thought of as a dict-like container for Series objects.

https://datatofish.com/data-type-pandas-dataframe/
Run the script in Python, and you'll get the following DataFrame: products prices sold_date 0 Product A 100 2023-11-01 1 Product B 250 2023-11-03 2 Product C 875 2023-11-05 Step 2: Check the Data Type. You can now check the data type of all columns in the DataFrame by adding df.dtypes to the script:

https://stackoverflow.com/questions/41973423/typeerror-dataframe-object-is-not-callable
Long story short, pandas DataFrames are objects of type 'DataFrame' whose attribute that makes an object callable is null. For example, in the OP, the culprit is: credit_card(col) because previously, credit_card was defined to be a pandas DataFrame object via. credit_card = pd.read_csv("default_of_credit_card_clients_Data.csv", skiprows=1)

https://www.typeerror.org/docs/pandas~1/reference/api/pandas.dataframe.le
pandas.DataFrame.le DataFrame.le(other, axis='columns', level=None)[source] Get Less than or equal to of dataframe and other, element-wise (binary operator le). Among flexible wrappers (eq, ne, le, lt, ge, gt) to comparison operators. Equivalent to ==, !=, <=, <, >=, > with support to choose axis (rows or columns) and level for comparison. Parameters other:scalar, sequence, Series, or

https://saturncloud.io/blog/how-to-set-dtypes-by-column-in-pandas-dataframe/
To set dtypes by column in a pandas DataFrame, we can use the astype() method. This method allows us to cast the data in a column to a specific dtype. Suppose we have a DataFrame df with columns A, B, and C. We want to set the dtype of column A to int, column B to float, and column C to str. Output:

https://tedboy.github.io/pandas/indexing/indexing7.html
2.7 Selection By Callable. New in version 0.18.1. .loc, .iloc, .ix and also [] indexing can accept a callable as indexer. The callable must be a function with one argument (the calling Series, DataFrame or Panel) and that returns valid output for indexing.

https://www.slingacademy.com/article/pandas-using-dataframe-with-type-hints/
Our first example focuses on the basic application of type hints by specifying column data types when creating a DataFrame. This approach enhances code readability and assists with data integrity. This function takes a list of dictionaries (each representing an employee) and returns a DataFrame. The type hints list[EmployeeData] and -> pd

https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.dtypes.html
pandas.DataFrame.dtypes #. pandas.DataFrame.dtypes. #. Return the dtypes in the DataFrame. This returns a Series with the data type of each column. The result's index is the original DataFrame's columns. Columns with mixed types are stored with the object dtype. See the User Guide for more.

https://dev.socrata.com/blog/2016/02/01/pandas-and-jupyter-notebook.html
The data is returned as a "DataFrame" which is a 2 dimensional spreadsheet-like data structure with columns of different types. pandas has two main data structures - DataFrame and Series. A Series is a one-dimensional array that can hold any value type - This is not necessarily the case but a DataFrame column may be treated as a Series.

https://tedboy.github.io/pandas/_modules/pandas/core/frame.html
The primary pandas data structure Parameters ---------- data : numpy ndarray (structured or homogeneous), dict, or DataFrame Dict can contain Series, arrays, constants, or list-like objects index : Index or array-like Index to use for resulting frame. Will default to np.arange (n) if no indexing information part of input data and no index

https://github.com/ydataai/ydata-profiling
Like pandas df.describe() function, that is so handy, ydata-profiling delivers an extended analysis of a DataFrame while allowing the data analysis to be exported in different formats such as html and json. The package outputs a simple and digested analysis of a dataset, including time-series and text.

https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.select_dtypes.html
pandas.DataFrame.select_dtypes. #. Return a subset of the DataFrame's columns based on the column dtypes. A selection of dtypes or strings to be included/excluded. At least one of these parameters must be supplied. The subset of the frame including the dtypes in include and excluding the dtypes in exclude. Return Series with the data type of

https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.prod.html
skipna bool, default True. Exclude NA/null values when computing the result. numeric_only bool, default False. Include only float, int, boolean columns. Not implemented for Series. min_count int, default 0. The required number of valid values to perform the operation.

https://pandas.pydata.org/docs/user_guide/gotchas.html
The special value np.nan (Not-A-Number) was chosen as the NA value for NumPy types, and there are API functions like DataFrame.isna() and DataFrame.notna() which can be used across the dtypes to detect NA values. However, this choice has a downside of coercing missing integer data as float types as shown in Support for integer NA.

https://perso.esiee.fr/~courivad/python_advanced/chapters/03-pandas.html
Il s'agit d'une introduction au concept de data frame dans l'environnement Python. Pour une documentation exhaustive, on consultera le User Guide. Pour démarrer, la première chose à faire est d'importer le module pandas. L'alias pd est traditionnellement utilisé: import pandas as pd. 4.1.

https://pandas.pydata.org/
pandas is a fast, powerful, flexible and easy to use open source data analysis and manipulation tool, built on top of the Python programming language. Install pandas now! Getting started

https://tedboy.github.io/pandas/enhancingperf/enhancingperf2.html
7.2.1 Jit. Using numba to just-in-time compile your code. We simply take the plain python code from above and annotate with the @jit decorator. Note that we directly pass numpy arrays to the numba function. compute_numba is just a wrapper that provides a nicer interface by passing/returning pandas objects. 1000 loops, best of 3: 798 us per loop.

https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.RangeIndex.html
RangeIndex is a memory-saving special case of an Index limited to representing monotonic ranges with a 64-bit dtype. Using RangeIndex may in some instances improve computing speed. This is the default index type used by DataFrame and Series when no explicit index is provided by the user. If int and "stop" is not given, interpreted as