Posts (page 52)
-
4 min readTo replace column values with NaN based on index with pandas, you can use the loc method to select rows based on index and column labels, and then assign them the value np.nan. Here is an example code snippet: import pandas as pd import numpy as np # Create a sample DataFrame data = {'A': [1, 2, 3, 4, 5], 'B': [6, 7, 8, 9, 10]} df = pd.DataFrame(data) # Replace values in column 'A' with NaN based on index df.loc[[1, 3], 'A'] = np.
-
4 min readIn pandas, you can convert time formats by using the to_datetime function. This function can convert a string representing a date and time into a datetime object. You can also specify the format of the input string using the format parameter. This is useful when the date and time format is different from the default format that pandas recognizes. Additionally, you can also use the strftime function to convert a datetime object to a string with a specific format.
-
6 min readTo sort and group on a column using a pandas loop, you can use the groupby() function to group your dataframe by a specific column and then apply the sort_values() function to sort the groups based on a different column. This can be done in a loop by iterating over the unique values in the column you want to group by, creating separate dataframes for each group, sorting the dataframes, and then concatenating the sorted dataframes back together.
-
5 min readTo read a specific column in an xlsx file using pandas, you can use the pd.read_excel() function to read the entire file into a DataFrame and then use bracket notation to access the desired column.For example, if you want to read the column named 'column_name' from an xlsx file called 'file.xlsx', you can use the following code: import pandas as pd # Read the excel file into a DataFrame df = pd.read_excel('file.
-
4 min readTo apply a function to specific columns in pandas, you can use the apply() method along with the axis parameter to specify whether you want to apply the function row-wise or column-wise. To apply a function to specific columns, you can use the apply() method along with the subset parameter to specify the columns you want to apply the function to. Additionally, you can use lambda functions to apply custom functions to specific columns in pandas.
-
5 min readTo print out the cell value from an Excel spreadsheet using Pandas, you can first import the Pandas library in your Python script. Then, load the Excel file into a Pandas DataFrame using the read_excel() function. Once you have the DataFrame, you can access individual cell values using the .at or .iat methods along with the row and column indexes. For example, to print out the value in the cell at row 1 and column 1, you can use print(df.at[1, 1]).
-
3 min readTo union 3 dataframes by pandas, you can use the concat() function. This function allows you to concatenate multiple dataframes along a specified axis (rows or columns). You can pass a list of dataframes as an argument to the function, and pandas will concatenate them together. The syntax for the concat() function is pd.concat([df1, df2, df3]), where df1, df2, and df3 are the dataframes you want to union.
-
4 min readTo create a new column that gets the count by groupby in pandas, you can use the following code: df['group_count'] = df.groupby('column_to_groupby')['column_to_count'].transform('count') This code will create a new column in the dataframe df called group_count that will contain the count of occurrences of each group in the column specified in column_to_count after grouping by the column specified in column_to_groupby.
-
7 min readTo iterate through a pandas series with 2 indexes, you can use the iteritems() method to iterate over the series and access both indexes and values. This method will return a generator that yields the index and value pairs as tuples. You can then loop through this generator and access both indexes and values at each iteration.
-
5 min readTo extract data inside a bracket in pandas, you can use the str.extract() function in combination with regular expressions. First, create a regular expression pattern that matches the data you want to extract inside the bracket. Then, use the str.extract() function on the column containing the data, passing the regular expression pattern as an argument. This will return a new column with the extracted data inside the bracket.
-
5 min readTo keep group by values for each row in a Pandas DataFrame, you can use the transform method. This allows you to maintain the grouping information for each row in the DataFrame without collapsing it into a summary statistic like sum or mean.By using the transform method, you can create a new column in the DataFrame that retains the group by values for each row.
-
3 min readTo open an XLS file with Pandas, you can use the pd.read_excel() function provided by the Pandas library. This function allows you to read data from Excel files and load it into a Pandas DataFrame. Simply provide the file path of the XLS file as an argument to the function to open and read the file. You can then manipulate and analyze the data in the DataFrame using Pandas functionalities.[rating:b1c44d88-9206-437e-9aff-ba3e2c424e8f]How to rename columns when opening an Excel file with pandas.