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4 min readIn pandas, merging with groupby involves combining two dataframes based on a common key and grouping the data based on that key. This is done using the merge() function along with the groupby() function in pandas.To perform a merge with groupby in pandas, you first need to group the dataframes by the common key using the groupby() function. Then, you can use the merge() function to combine the groupby objects based on the specified keys.
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4 min readTo convert a CSV file to a Parquet file using pandas, you can follow these steps:First, import the pandas library in your Python script. Read the CSV file into a pandas DataFrame using the read_csv() function. Use the to_parquet() function to save the DataFrame as a Parquet file. Specify the file path where you want to save the Parquet file. Run the script to convert the CSV file to a Parquet file.
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5 min readTo get the previous item in a pandas dataframe, you can use the shift() method with a negative value as the parameter. For example, to get the previous item in a specific column, you can use df['column_name'].shift(-1). This will shift the values in the column by one position, effectively giving you the previous item in the dataframe.[rating:b1c44d88-9206-437e-9aff-ba3e2c424e8f]What is the output format of the previous item in a pandas dataframe.
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4 min readTo count the number of null values per year using Pandas, you can use the following approach:Create a new column in your DataFrame that contains the year extracted from the datetime column.Use the groupby() function to group the data by the year column.Use the isnull() function to check for null values in each group.Use the sum() function to count the number of null values in each group.
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4 min readTo get a pandas dataframe using PySpark, you can first create a PySpark dataframe from your data using the PySpark SQL module. Then, you can use the toPandas() function to convert the PySpark dataframe into a pandas dataframe. This function will collect all the data from the PySpark dataframe into the driver node of the Spark cluster and convert it into a pandas dataframe.
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3 min readThe to_sql method in pandas allows you to write a DataFrame directly to a SQL database table. This can be useful for saving data from your analysis in pandas to a database for easier access or sharing with others.To use to_sql, you first need to have a SQLAlchemy engine that points to your database. You can create an engine using a connection string that specifies the database type, username, password, and database name.
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5 min readTo rename a column in pandas when the column name contains a space, you can use the rename function and specify the old column name with the space enclosed in quotes. For example, if you have a DataFrame df with a column named "First Name", you can rename it to "First_Name" by using the following syntax: df.rename(columns={'First Name': 'First_Name'}, inplace=True) This will rename the column with a space to a column with an underscore in the name.
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5 min readTo use a variable as the value of the replace function in Python pandas, you can simply assign the variable to the value parameter of the replace method. For example, if you have a DataFrame df and a variable value_to_replace that stores the value you want to replace, you can use the following syntax: df.replace(value_to_replace, new_value, inplace=True) This will replace all occurrences of the value stored in the variable value_to_replace with the new_value in the DataFrame df.
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5 min readIn pandas, you can assign new columns based on chaining by using the .assign() method. This method allows you to add new columns to a DataFrame by specifying the column name and the values for the new column.For example, you can chain multiple .assign() calls together to create multiple new columns in one go. This can be achieved by using the assignment operator (=) to assign new values to the existing columns or create new columns based on the existing ones.
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5 min readIn Pandas, if you have a string column containing a dictionary and you want to convert it into a dictionary column, you can use the ast module to help with this conversion. First, you need to import the ast module by using import ast. Then, you can apply the ast.literal_eval() function on the string column to convert the strings into dictionaries.
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5 min readTo compare two lists of pandas dataframes, you can use the equals() method provided by pandas. This method allows you to check if two dataframes are equal by comparing their values. Additionally, you can also use other methods like isin() to check if the values of one dataframe are present in the other dataframe. These methods can help you identify similarities and differences between the two lists of dataframes.