TopMiniSite
-
7 min readTo loop through each row of a pandas dataframe, you can use the iterrows() method. This method returns an iterator that yields index and row data as a Series. You can then iterate over this iterator and access the values in each row using key-value pairs. Here's an example: import pandas as pd # Create a sample dataframe data = {'A': [1, 2, 3], 'B': [4, 5, 6]} df = pd.DataFrame(data) # Loop through each row of the dataframe for index, row in df.
-
4 min readTo sort each row data using pandas, you can use the sort_values method along the axis parameter axis=1. This will sort the values of each row in ascending or descending order. Additionally, you can specify the ascending=False argument to sort in descending order. For example, you can sort a DataFrame named df by each row using the following code: df.sort_values(by=df.columns.
-
5 min readTo summarize rows on a specific column in a pandas dataframe, you can use the groupby function along with the aggregate method.First, you need to specify the column you want to group by using the groupby function. Then, you can use the aggregate method to apply one or more aggregation functions, such as mean, sum, count, etc., to the grouped data.
-
3 min readTo find common substrings in a pandas dataframe, you can use the str.contains() method along with regular expressions. First, select the column you want to search for substrings in, then use the str.contains() method with your desired pattern as an argument to filter the rows that contain the substring. You can then retrieve the common substrings by examining the filtered dataframe.
-
4 min readTo delete every 5 rows in a pandas DataFrame, you can use the drop method with the iloc indexer. Here's an example code snippet: import pandas as pd # Create a sample DataFrame data = {'A': range(1, 101)} df = pd.DataFrame(data) # Delete every 5th row df = df.drop(df.index[::5]) # Print the modified DataFrame print(df) In this code, we create a sample DataFrame with values in column 'A' ranging from 1 to 100. We then use the drop method along with the slicing syntax df.
-
3 min readTo remove header names from each row in a pandas dataframe, you can use the rename_axis function with the parameter None to remove the header names. This will set the header names to None for each row in the dataframe.[rating:562d6693-f62e-4918-b72b-b7c41ecdb54b]How can I erase column titles in pandas dataframe?You can reset the column names in a pandas dataframe by setting the columns attribute to None.
-
5 min readTo separate elements in a pandas dataframe, you can use various methods such as indexing, selection, or filtering.One common method is to use the loc or iloc functions to select specific rows or columns based on their indices. For example, you can separate rows by using the loc function with a specific row index or iloc function with a range of row indices.You can also separate elements by filtering the dataframe based on specific conditions.
-
4 min readIn pandas, you can access keys in a DataFrame using square brackets. You can access individual columns by passing the column name inside the square brackets, like df['column_name'].To access multiple columns, you can pass a list of column names inside the square brackets, like df[['column_name1', 'column_name2']].You can also access rows by using the iloc or loc methods.
-
5 min readThere are a few different ways to move data from a SQL Server database to an Oracle database. One common method is to use Oracle's SQL Developer tool, which has a built-in feature for migrating data from third-party databases. This tool allows you to connect to both your SQL Server and Oracle databases, select the tables you want to transfer, and then run a data migration wizard to move the data.
-
3 min readIn pandas, you can style a column based on a condition using the Styler class which allows you to apply various styles to your DataFrame.To style a column based on a condition, you first create a function that defines the condition and then use the applymap method from the Styler class to apply your custom function to the DataFrame.For example, let's say you have a DataFrame df and you want to style the column 'A' based on a condition where the value is greater than 0.
-
5 min readTo insert a zero instead of a null in Oracle, you can use the NVL function. The NVL function allows you to specify a default value to replace any null values in a column.