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To convert a nested dictionary to a pandas dataframe, you can first flatten the nested dictionary using a function like json_normalize from the pandas library. This function can create a flat table from a nested JSON object.First, import pandas and then use the json_normalize function to flatten the nested dictionary. You can pass the nested dictionary to this function, and it will create a dataframe with all the nested keys as columns.For example: import pandas as pd from pandas.io.

To split a string using multiple characters in pandas, you can use the 'str.split()' method and specify the characters you want to split on as a regular expression pattern. For example, if you want to split a string on both '-' and '_', you can use the following code:df['column'].str.split(r'[-_]')This will split the string in the specified column of the dataframe 'df' on both '-' and '_' characters.

To add a new column based on a boolean list in pandas, you can simply create a new column and assign the boolean list to it. This can be done by using the following code: import pandas as pd # Create a DataFrame data = {'A': [1, 2, 3, 4, 5], 'B': [10, 20, 30, 40, 50]} df = pd.

To combine two pandas series, you can use the pandas.concat() function. This function concatenates two series along a specified axis, either rows or columns. By default, the function concatenates along rows.For example, to combine two series along columns, you can set the axis parameter to 1: combined_series = pd.concat([series1, series2], axis=1) This will combine the two series into a single DataFrame where each series is a separate column.

To change the column names of a pandas series object, you can use the rename() method. You can pass a dictionary to the columns parameter of the rename() method where the keys are the old column names and the values are the new column names you want to assign. For example, if you have a series object named "s" with column name "A" and you want to change it to "B", you can use the code s.rename(columns={'A':'B'}).

To merge two data frames using a condition in pandas, you can use the merge function along with the on parameter to specify the column(s) to merge on. You can also use the how parameter to specify the type of join (e.g. inner, outer, left, right).For example, if you have two data frames df1 and df2 and you want to merge them based on a condition where a column in df1 is equal to a column in df2, you can use the following code: merged_df = pd.

To create a rank from a DataFrame using pandas, you can use the rank() function. This function assigns a rank to each element in the DataFrame based on their values. By default, elements with the same value are assigned the same rank, and the next rank is skipped.You can specify the method parameter of the rank() function to change how ties are handled. The available methods include "average", "min", "max", "first", and "dense".

To convert a dictionary of lists into a pandas dataframe, you can simply pass the dictionary as an argument to the pandas DataFrame constructor. Each key-value pair in the dictionary will be treated as a column in the dataframe, with the key becoming the column name and the list becoming the values in that column. This allows you to easily work with and manipulate the data using pandas' powerful functionalities.

To add dictionary items in a pandas column, you can first create a pandas DataFrame and then assign the dictionary as a value to a specific column. For example, you can create a DataFrame like this: import pandas as pd data = {'col1': [1, 2, 3, 4], 'col2': [{'key1': 'value1'}, {'key2': 'value2'}, {'key3': 'value3'}, {'key4': 'value4'}]} df = pd.

To aggregate between two dataframes in pandas, you can use the merge function. This function allows you to combine data from two dataframes based on a shared column or index. You can specify the type of merge (inner, outer, left, right) to determine how the data will be combined.For example, if you have two dataframes df1 and df2, and you want to aggregate the data based on a common column 'key', you can use the merge function like this: result = pd.