Develop Reference

How to iterate through for loop and write each dataframe to different excel sheets

I'm looping through list of jsons and storing them in a dataframe. for each iteration i want to write the dataframe into excel with different sheets. how to achieve this?
for item in data:
#removing empty columns in raw data
drop_none = lambda path, key, value: key is not None and value is not None
cleaned = remap(item, visit=drop_none)
new_data=flatten(cleaned)
#my_df = new_data.dropna(axis='columns', how='all') # Drops columns with all NA values
dfFromRDD2 = spark.createDataFrame(new_data)
I want to save the dataframe dfFromRDD2 to excel with different sheets on each iterations.
is their a way to do it using python?

Mapping in excel using python

Excel 1
Group Summary Label Amount
Individual Member
Family Member
Family
Excel 2
Network Label Value
Individual Member 100
Family Member 200
Family 300
I have two Excel sheets and I am trying to map values. As you see both excels has different column names but rows are same. I am trying to map 'value' in excel 2 to Amount in excel 1
I am expecting result like this. How can I do this using Python? I am new and trying to learn.
Group Summary Label Amount
Individual Member 100
Family Member 200
Family 300

First, open two dataframe of each sheet inside the excel:
df = pd.ExcelFile('excelfilename.xls')
df1 = pd.read_excel(df, 'Sheet1')
df2 = pd.read_excel(df, 'Sheet2')
now you can match the two dataFrames into one
df1.drop(["Amount"], axis=1)
df2 = df2.join(df1)
df2 = df2.rename(columns={"Value": "Amount"}, errors="raise")

How to sum over sheets that have same index in pandas?

I have an excel data containing multiple sheets.
The data is hourly rainfall data value that have spatial index longitude in row and latitude in column.
This is the excel file
I need to sum over all these sheets to get the daily rainfall data.
How could I do with pandas in python?

You can use dict = pd.read_excel('myfile.xlsx', sheetname=None). This function gives you a dictionary of sheets that iterate by for sheet in dict.items(). The rest is same as the solution that #cs95 has provided. Concat them inside the for loop and then group by lat/long.

Fill an existing Excel file with data from a Pandas DataFrame

I have a Pandas DataFrame with a bunch of rows and labeled columns.
I also have an excel file which I prepared with one sheet which contains no data but only
labeled columns in row 1 and each column is formatted as it should be: for example if I
expect percentages in one column then that column will automatically convert a raw number to percentage.
What I want to do is fill the raw data from my DataFrame into that Excel sheet in such a way
that row 1 remains intact so the column names remain. The data from the DataFrame should fill
the excel rows starting from row 2 and the pre-formatted columns should take care of converting
the raw numbers to their appropriate type, hence filling the data should not override the column format.
I tried using openpyxl but it ended up creating a new sheet and overriding everything.
Any help?

If you're certain about the order of columns is same, you can try this after opening the sheet with openpyxl:
df.to_excel(writer, startrow = 2,index = False, Header = False)

If your # of columns and order is same then you may try xlsxwriter and also mention the sheet name to want to refresh:
df.to_excel('filename.xlsx', engine='xlsxwriter', sheet_name='sheetname', index=False)

Search entire excel sheet with Pandas for word(s)

I am trying to essentially replicate the Find function (control-f) in Python with Pandas. I want to search and entire sheet (all rows and columns) to see if any of the cells on the sheet contain a word and then print out the row in which the word was found. I'd like to do this across multiple sheets as well.
I've imported the sheet:
pdTestDataframe = pd.read_excel(TestFile, sheet_name="Sheet Name",
keep_default_na= False, na_values=[""])
And tried to create a list of columns that I could index into the values of all of the cells but it's still excluding many of the cells in the sheet. The attempted code is below.
columnsList = []
for i, data in enumerate(pdTestDataframe.columns):
columnList.append(pdTestDataframe.columns[i])
for j, data1 in enumerate(pdTestDataframe.index):
print(pdTestDataframe[columnList[i]][j])
I want to make sure that no matter the formatting of the excel sheet, all cells with data inside can be searched for the word(s). Would love any help I can get!

Pandas has a different way of thinking about this. Just calling df[df.text_column.str.contains('whatever')] will show you all the rows in which the text is contained in one specific column. To search the entire dataframe, you can use:
mask = np.column_stack([df[col].str.contains(r"\^", na=False) for col in df])
df.loc[mask.any(axis=1)]
(Source is here)