How to wrap text in excel using Python?
Question:
I created a script which parse YAML files to Excel. I’ve made part of code which adjust width of column but now I want to wrap text for Desciprtion column.
After script execution:
What I want:
Here is my code:
import yaml
import os
import pandas as pd
from openpyxl.utils import get_column_letter
import sys
def yaml_to_excel(dictionary, row):
for key, value in dictionary.items():
if type(value) is dict:
yield from yaml_to_excel(value, row + 1)
elif type(value) is list:
for item in value:
yield from yaml_to_excel(item, row + 1)
row += 1
else:
yield (key, value, row)
def get_all_subdirectiries_in_path(root_dir):
all_subdirectories = []
for file in os.listdir(root_dir):
d = os.path.join(root_dir, file)
if os.path.isdir(d):
all_subdirectories.append(d)
return all_subdirectories
def dfs_tabs(df_list, sheet_list, file_name, columns):
writer = pd.ExcelWriter(file_name,engine='openpyxl')
for dataframe, sheet, columns_list in zip(df_list, sheet_list, columns):
dataframe.to_excel(writer, sheet_name=sheet, startrow=0 , startcol=0, index=False, columns=columns_list)
for column in dataframe:
column_width = max(dataframe[column].astype(str).map(len).max() + 5, len(column) + 5)
col_letter = get_column_letter(dataframe.columns.get_loc(column) + 1)
writer.sheets[sheet].column_dimensions[col_letter].width = column_width
writer.save()
def get_nested_dir_path(main_dir):
if any(File.endswith(".yml") for File in os.listdir(main_dir)):
yield main_dir
else:
for file in os.listdir(main_dir):
yield from get_nested_dir_path(main_dir + f"/{file}")
def main(dir_path):
all_sheets = []
sheet_names = []
all_columns = []
for subdirectory in get_nested_dir_path(dir_path):
final_object = []
last_row = 1
current_row = 1
for file in os.listdir(subdirectory):
# Check if YAML file exist
if (file.split(".")[1] == "yml") | (file.split(".")[1] == "yaml"):
with open(subdirectory + f"/{file}", "r", encoding="utf-8") as f:
dataMap = yaml.safe_load(
f,
)
last_row += 1
hierarchy_obj = {}
hierarchy = 1
prev_value = ""
prev_temp_depth = 2
current_row += 1
starting_row = current_row
for key, value, temp_depth in yaml_to_excel(dataMap, last_row):
if key not in hierarchy_obj:
hierarchy_obj[key] = hierarchy
hierarchy += 1
if prev_value != "":
if hierarchy_obj[key] < hierarchy_obj[prev_value]:
current_row += 1
if (temp_depth > prev_temp_depth) & (
hierarchy_obj[key] > hierarchy_obj[prev_value]
):
current_row += 1
if type(value) is bool:
if value:
value = 'yes'
else:
value = 'no'
final_object.append(
{
"column": hierarchy_obj[key],
"value": value,
"row": current_row,
}
)
prev_value = key
prev_temp_depth = temp_depth
columns_arr = []
for column_name in hierarchy_obj.keys():
columns_arr.append(column_name)
all_columns.append(columns_arr)
last_row_in_excel = final_object[len(final_object)-1]['row']
main_arr = []
for i in range(last_row_in_excel):
main_arr.append(["" for i in range(len(columns_arr))])
main_arr = [main_arr]
for item in final_object:
if (type(item['value']) == int) | (type(item['value']) == float):
main_arr[0][item['row'] - 1][item['column'] - 1] = item['value']
else:
main_arr[0][item['row'] - 1][item['column'] -1] = item['value']
result = pd.DataFrame(main_arr[0], columns = columns_arr)
result = result[1:]
all_sheets.append(result)
sheet_names.append(subdirectory[subdirectory.rfind('/')+1 :].replace("/", " ").replace("_", " ")[0:31])
dfs_tabs(all_sheets, sheet_names, 'test.xlsx', all_columns)
if __name__ == "__main__":
dir_path = 'Lands'
main(dir_path)
And below you can find YAML file which I base of:
Country: France
Capital City: Paris
Language: French
Description: |-
A country whose metropolitan part is in Western Europe, and also has overseas territories on other continents.
The most important people:
President: Emmanuel Macron
Prime Minister: Élisabeth Borne
Year: 2020
Population:
- Total population: 67 522 000
Year: 2021
Nationality:
- French population: 87%
German population: 2%
Year: 2022
Paris neighborhoods:
- 1st district of Paris: Louvre
Paris II district: Bourse
Year: 2023
Additional information:
To run my script you must create folder Lands/France/france.yml
I will be grateful for any tips how to wrap text in Description column!
Answers:
I suggest you in this particular situation to parse the .YAML file with pandas.json_normalize and use the format class of xlswriter to wrap the text in the column Description.
Try this :
import pandas as pd
from yaml import safe_load
with open(r"LandsFrancefrance.yaml", 'r', encoding='utf-8') as f:
df = pd.json_normalize(safe_load(f))
# --- Selecting columns
description_index = df.columns.get_loc("Description")
df.iloc[:, 0:description_index+1]
with pd.ExcelWriter('final_result.xlsx') as writer :
df.to_excel(writer, sheet_name='Sheet1', index=False)
# --- Wrapping the text of column D
workbook=writer.book
worksheet = writer.sheets['Sheet1']
format = workbook.add_format({'text_wrap': True})
worksheet.set_column(description_index, description_index, None, format)
# --- Auto-fit all the columns
for column in df:
column_width = max(df[column].astype(str).map(len).max(), len(column))
col_idx = df.columns.get_loc(column)
worksheet.set_column(col_idx, col_idx, column_width)
# Output (in Excel) :
I created a script which parse YAML files to Excel. I’ve made part of code which adjust width of column but now I want to wrap text for Desciprtion column.
After script execution:
What I want:
Here is my code:
import yaml
import os
import pandas as pd
from openpyxl.utils import get_column_letter
import sys
def yaml_to_excel(dictionary, row):
for key, value in dictionary.items():
if type(value) is dict:
yield from yaml_to_excel(value, row + 1)
elif type(value) is list:
for item in value:
yield from yaml_to_excel(item, row + 1)
row += 1
else:
yield (key, value, row)
def get_all_subdirectiries_in_path(root_dir):
all_subdirectories = []
for file in os.listdir(root_dir):
d = os.path.join(root_dir, file)
if os.path.isdir(d):
all_subdirectories.append(d)
return all_subdirectories
def dfs_tabs(df_list, sheet_list, file_name, columns):
writer = pd.ExcelWriter(file_name,engine='openpyxl')
for dataframe, sheet, columns_list in zip(df_list, sheet_list, columns):
dataframe.to_excel(writer, sheet_name=sheet, startrow=0 , startcol=0, index=False, columns=columns_list)
for column in dataframe:
column_width = max(dataframe[column].astype(str).map(len).max() + 5, len(column) + 5)
col_letter = get_column_letter(dataframe.columns.get_loc(column) + 1)
writer.sheets[sheet].column_dimensions[col_letter].width = column_width
writer.save()
def get_nested_dir_path(main_dir):
if any(File.endswith(".yml") for File in os.listdir(main_dir)):
yield main_dir
else:
for file in os.listdir(main_dir):
yield from get_nested_dir_path(main_dir + f"/{file}")
def main(dir_path):
all_sheets = []
sheet_names = []
all_columns = []
for subdirectory in get_nested_dir_path(dir_path):
final_object = []
last_row = 1
current_row = 1
for file in os.listdir(subdirectory):
# Check if YAML file exist
if (file.split(".")[1] == "yml") | (file.split(".")[1] == "yaml"):
with open(subdirectory + f"/{file}", "r", encoding="utf-8") as f:
dataMap = yaml.safe_load(
f,
)
last_row += 1
hierarchy_obj = {}
hierarchy = 1
prev_value = ""
prev_temp_depth = 2
current_row += 1
starting_row = current_row
for key, value, temp_depth in yaml_to_excel(dataMap, last_row):
if key not in hierarchy_obj:
hierarchy_obj[key] = hierarchy
hierarchy += 1
if prev_value != "":
if hierarchy_obj[key] < hierarchy_obj[prev_value]:
current_row += 1
if (temp_depth > prev_temp_depth) & (
hierarchy_obj[key] > hierarchy_obj[prev_value]
):
current_row += 1
if type(value) is bool:
if value:
value = 'yes'
else:
value = 'no'
final_object.append(
{
"column": hierarchy_obj[key],
"value": value,
"row": current_row,
}
)
prev_value = key
prev_temp_depth = temp_depth
columns_arr = []
for column_name in hierarchy_obj.keys():
columns_arr.append(column_name)
all_columns.append(columns_arr)
last_row_in_excel = final_object[len(final_object)-1]['row']
main_arr = []
for i in range(last_row_in_excel):
main_arr.append(["" for i in range(len(columns_arr))])
main_arr = [main_arr]
for item in final_object:
if (type(item['value']) == int) | (type(item['value']) == float):
main_arr[0][item['row'] - 1][item['column'] - 1] = item['value']
else:
main_arr[0][item['row'] - 1][item['column'] -1] = item['value']
result = pd.DataFrame(main_arr[0], columns = columns_arr)
result = result[1:]
all_sheets.append(result)
sheet_names.append(subdirectory[subdirectory.rfind('/')+1 :].replace("/", " ").replace("_", " ")[0:31])
dfs_tabs(all_sheets, sheet_names, 'test.xlsx', all_columns)
if __name__ == "__main__":
dir_path = 'Lands'
main(dir_path)
And below you can find YAML file which I base of:
Country: France
Capital City: Paris
Language: French
Description: |-
A country whose metropolitan part is in Western Europe, and also has overseas territories on other continents.
The most important people:
President: Emmanuel Macron
Prime Minister: Élisabeth Borne
Year: 2020
Population:
- Total population: 67 522 000
Year: 2021
Nationality:
- French population: 87%
German population: 2%
Year: 2022
Paris neighborhoods:
- 1st district of Paris: Louvre
Paris II district: Bourse
Year: 2023
Additional information:
To run my script you must create folder Lands/France/france.yml
I will be grateful for any tips how to wrap text in Description column!
I suggest you in this particular situation to parse the .YAML file with pandas.json_normalize and use the format class of xlswriter to wrap the text in the column Description.
Try this :
import pandas as pd
from yaml import safe_load
with open(r"LandsFrancefrance.yaml", 'r', encoding='utf-8') as f:
df = pd.json_normalize(safe_load(f))
# --- Selecting columns
description_index = df.columns.get_loc("Description")
df.iloc[:, 0:description_index+1]
with pd.ExcelWriter('final_result.xlsx') as writer :
df.to_excel(writer, sheet_name='Sheet1', index=False)
# --- Wrapping the text of column D
workbook=writer.book
worksheet = writer.sheets['Sheet1']
format = workbook.add_format({'text_wrap': True})
worksheet.set_column(description_index, description_index, None, format)
# --- Auto-fit all the columns
for column in df:
column_width = max(df[column].astype(str).map(len).max(), len(column))
col_idx = df.columns.get_loc(column)
worksheet.set_column(col_idx, col_idx, column_width)
# Output (in Excel) :