Hi I am trying to create new columns to a multi-indexed pandas pivot table to do a countif statement (similar to excel) depending if a level of the index contains a specific string. This is the sample data:
df = pd.DataFrame({'City': ['Houston', 'Austin', 'Hoover','Adak','Denver','Houston','Adak','Denver'],
'State': ['Texas', 'Texas', 'Alabama','Alaska','Colorado','Texas','Alaska','Colorado'],
'Name':['Aria', 'Penelope', 'Niko','Susan','Aria','Niko','Aria','Niko'],
'Unit':['Sales', 'Marketing', 'Operations','Sales','Operations','Operations','Sales','Operations'],
'Assigned':['Yes','No','Maybe','No','Yes','Yes','Yes','Yes']},
columns=['City', 'State', 'Name', 'Unit','Assigned'])
pivot=df.pivot_table(index=['City','State'],columns=['Name','Unit'],values=['Assigned'],aggfunc=lambda x:', '.join(set(x)),fill_value='')
and this is the desired output (in screenshot). Thanks in advance!
try:
temp = pivot[('Mango', 'Aria', 'Sales')].str.len()>0
pivot['new col'] = temp.astype(int)
the result:
Based on your edit:
import numpy as np
temp = pivot.xs('Sales', level=2, drop_level=False, axis = 1).apply(lambda x: np.sum([1 if y!='' else 0 for y in x]), axis = 1)
pivot[('', 'total sales', 'count how many...')]=temp
Related
I have the following task.
I have this data:
import pandas
import numpy as np
data = {'name': ['Todd', 'Chris', 'Jackie', 'Ben', 'Richard', 'Susan', 'Joe', 'Rick'],
'phone': [912341.0, np.nan , 912343.0, np.nan, 912345.0, 912345.0, 912347.0, np.nan],
' email': ['todd#gmail.com', 'chris#gmail.com', np.nan, 'ben#gmail.com', np.nan ,np.nan , 'joe#gmail.com', 'rick#gmail.com'],
'most_visited_airport': ['Heathrow', 'Beijing', 'Heathrow', np.nan, 'Tokyo', 'Beijing', 'Tokyo', 'Heathrow'],
'most_visited_place': ['Turkey', 'Spain',np.nan , 'Germany', 'Germany', 'Spain',np.nan , 'Spain']
}
df = pandas.DataFrame(data)
What I have to do is for every feature column (most_visited_airport etc.) and its values (Heathrow, Beijing, Tokyo) I have to generate personal information and output it to a file.
E.g. If we look at most_visited_airport and Heathrow
I need to output three files containing the names, emails and phones of the people who visited the airport the most.
Currently, I have this code to do the operation for both columns and all the values:
columns_to_iterate = [ x for x in df.columns if 'most' in x]
for each in df[columns_to_iterate]:
values = df[each].dropna().unique()
for i in values:
df1 = df.loc[df[each]==i,'name']
df2 = df.loc[df[each]==i,' email']
df3 = df.loc[df[each]==i,'phone']
df1.to_csv(f'{each}_{i}_{df1.name}.csv')
df2.to_csv(f'{each}_{i}_{df2.name}.csv')
df3.to_csv(f'{each}_{i}_{df3.name}.csv')
Is it possible to do this in a more elegant and maybe faster way? Currently I have small dataset but not sure if this code will perform well with big data. My particular concern are the nested loops.
Thank you in advance!
You could replace the call to unique with a groupby, which would not only get the unique values, but split up the dataframe for you:
for column in df.filter(regex='^most'):
for key, group in df.groupby(column):
for attr in ('name', 'phone', 'email'):
group['name'].dropna().to_csv(f'{column}_{key}_{attr}.csv')
You can do it this way.
cols = df.filter(regex='most').columns.values
def func_current_cols_to_csv(most_col):
place = [i for i in df[most_col].dropna().unique().tolist()]
csv_cols = ['name', 'phone', ' email']
result = [df[df[most_col] == i][j].dropna().to_csv(f'{most_col}_{i}_{j}.csv', index=False) for i in place for j in
csv_cols]
return result
[func_current_cols_to_csv(i) for i in cols]
also in the options when writing to csv, you can leave the index, but do not forget to reset it before writing.
I have a dataset where I have to drop rows with multiple columns. I tried this, but do not know how to do with multiple values
import pandas as pd
df = pd.read_csv("data.csv")
new_df = df[df.location == 'New York' ]
new_df.count()
I also tried another method, but do not know, how to do with multiple values:
import pandas as pd
df = pd.read_csv("data.csv")
df.drop(df[df['location '] == 'New York'].index, inplace = True)
I have delete rows, with values new york, boston, Austin and keep other locations remaining.
Also, I have replace value of a column
if San Francisco then change value to 1, if Miami change to 2, so all values in location, should be replaced
You can use query method and variable with all cities you want to filter
np.random.seed(0)
cities = ['New York', 'Chicago', 'Miami']
data = pd.DataFrame(dict(cities = np.random.choice(cities, 10),
values = np.random.choice(10,10)))
data.cities.unique() # array(['New York', 'Chicago', 'Miami'], dtype=object)
filter = ['New York', 'Chicago']
data_filtered = data.query('cities not in #filter').copy()
data_filtered.cities.unique() # array(['Miami'], dtype=object)
For the values, you can manually set values
data_filtered.loc[data_filtered.cities == 'Miami', ['values']] =2
I don't quite follow what you mean by dropping rows with multiple columns, but to check for multiple values you could use: new_df = df[df.location in ['New York', 'Boston']]
You can try:
# Drop the rows with location "New York", "Boston", "Austin" (1)
df = df[~df["location"].isin(["New York", "Boston", "Austin"])]
# Replace locations with numbers: (2)
loc_map = {"San Francisco": 1, "Miami": 2, ...}
df["location"] = df["location"].map(loc_map)
For step (2), in case you have many values, you can create loc_map automatically by:
loc_map = {df.location.unique()[i]: i+1 for i in range(len(df.location.unique()))}
Hope this helps.
Apologies, I didn't even know how to title/describe the issue I am having, so bear with me. I have the following code:
import pandas as pd
data = {'Invoice Number':[1279581, 1279581,1229422, 1229422, 1229422],
'Project Key':[263736, 263736, 259661, 259661, 259661],
'Project Type': ['Visibility', 'Culture', 'Spend', 'Visibility', 'Culture']}
df= pd.DataFrame(data)
How do I get the output to basically group the Invoice Numbers so that there is only 1 row per Invoice Number and combine the multiple Project Types (per that 1 Invoice) into 1 row?
Code and output for output is below.
Thanks much appreciated.
import pandas as pd
data = {'Invoice Number':[1279581,1229422],
'Project Key':[263736, 259661],
'Project Type': ['Visibility_Culture', 'Spend_Visibility_Culture']
}
output = pd.DataFrame(data)
output
>>> (df
.groupby(['Invoice Number', 'Project Key'])['Project Type']
.apply(lambda x: '_'.join(x))
.reset_index()
)
Invoice Number Project Key Project Type
0 1229422 259661 Spend_Visibility_Culture
1 1279581 263736 Visibility_Culture
I have created a class with two methods, NRG_load and NRG_flat. The first loads a CSV, converts it into a DataFrame and applies some filtering; the second takes this DataFrame and, after creating two columns, it melts the DataFrame to pivot it.
I am trying out these methods with the following code:
nrg105 = eNRG.NRG_load('nrg_105a.tsv')
nrg105_flat = eNRG.NRG_flat(nrg105, '105')
where eNRG is the class, and '105' as second argument is needed to run an if-loop within the method to create the aforementioned columns.
The behaviour I cannot explain is that the second line - the one with the NRG_flat method - changes the nrg105 values.
Note that if I only run the NRG_load method, I get the expected DataFrame.
What is the behaviour I am missing? Because it's not the first time I apply a syntax like that, but I never had problems, so I don't know where I should look at.
Thank you in advance for all of your suggestions.
EDIT: as requested, here is the class' code:
# -*- coding: utf-8 -*-
"""
Created on Tue Apr 16 15:22:21 2019
#author: CAPIZZI Filippo Antonio
"""
import pandas as pd
from FixFilename import FixFilename as ff
from SplitColumn import SplitColumn as sc
from datetime import datetime as ddt
class EurostatNRG:
# This class includes the modules needed to load and filter
# the Eurostat NRG files
# Default countries' lists to be used by the functions
COUNTRIES = [
'EU28', 'AL', 'AT', 'BE', 'BG', 'CY', 'CZ', 'DE', 'DK', 'EE', 'EL',
'ES', 'FI', 'FR', 'GE', 'HR', 'HU', 'IE', 'IS', 'IT', 'LT', 'LU', 'LV',
'MD', 'ME', 'MK', 'MT', 'NL', 'NO', 'PL', 'PT', 'RO', 'SE', 'SI', 'SK',
'TR', 'UA', 'UK', 'XK'
]
# Default years of analysis
YEARS = list(range(2005, int(ddt.now().year) - 1))
# NOTE: the 'datetime' library will call the current year, but since
# the code is using the 'range' function, the end years will be always
# current-1 (e.g. if we are in 2019, 'current year' will be 2018).
# Thus, I have added "-1" because the end year is t-2.
INDIC_PROD = pd.read_excel(
'./Datasets/VITO/map_nrg.xlsx',
sheet_name=[
'nrg105a_indic', 'nrg105a_prod', 'nrg110a_indic', 'nrg110a_prod',
'nrg110'
],
convert_float=True)
def NRG_load(dataset, countries=COUNTRIES, years=YEARS, unit='ktoe'):
# This module will load and refine the NRG dataset,
# preparing it to be filtered
# Fix eventual flags
dataset = ff.fix_flags(dataset)
# Load the dataset into a DataFrame
df = pd.read_csv(
dataset,
delimiter='\t',
encoding='utf-8',
na_values=[':', ': ', ' :'],
decimal='.')
# Clean up spaces from the column names
df.columns = df.columns.str.strip()
# Removes the mentioned column because it's not needed
if 'Flag and Footnotes' in df.columns:
df.drop(columns=['Flag and Footnotes'], inplace=True)
# Split the first column into separate columns
df = sc.nrg_split_column(df)
# Rename the columns
df.rename(
columns={
'country': 'COUNTRY',
'fuel_code': 'KEY_PRODUCT',
'nrg_code': 'KEY_INDICATOR',
'unit': 'UNIT'
},
inplace=True)
# Filter the dataset
df = EurostatNRG.NRG_filter(
df, countries=countries, years=years, unit=unit)
return df
def NRG_filter(df, countries, years, unit):
# This module will filter the input DataFrame 'df'
# showing only the 'countries', 'years' and 'unit' selected
# First, all of the units not of interest are removed
df.drop(df[df.UNIT != unit.upper()].index, inplace=True)
# Then, all of the countries not of interest are filtered out
df.drop(df[~df['COUNTRY'].isin(countries)].index, inplace=True)
# Finally, all of the years not of interest are removed,
# and the columns are rearranged according to the desired output
main_cols = ['KEY_INDICATOR', 'KEY_PRODUCT', 'UNIT', 'COUNTRY']
cols = main_cols + [str(y) for y in years if y not in main_cols]
df = df.reindex(columns=cols)
return df
def NRG_flat(df, name):
# This module prepares the DataFrame to be flattened,
# then it gives it as output
# Assign the indicators and products' names
if '105' in name: # 'name' is the name of the dataset
# Creating the 'INDICATOR' column
indic_dic = dict(
zip(EurostatNRG.INDIC_PROD['nrg105a_indic'].KEY_INDICATOR,
EurostatNRG.INDIC_PROD['nrg105a_indic'].INDICATOR))
df['INDICATOR'] = df['KEY_INDICATOR'].map(indic_dic)
# Creating the 'PRODUCT' column
prod_dic = dict(
zip(
EurostatNRG.INDIC_PROD['nrg105a_prod'].KEY_PRODUCT.astype(
str), EurostatNRG.INDIC_PROD['nrg105a_prod'].PRODUCT))
df['PRODUCT'] = df['KEY_PRODUCT'].map(prod_dic)
elif '110' in name:
# Creating the 'INDICATOR' column
indic_dic = dict(
zip(EurostatNRG.INDIC_PROD['nrg110a_indic'].KEY_INDICATOR,
EurostatNRG.INDIC_PROD['nrg110a_indic'].INDICATOR))
df['INDICATOR'] = df['KEY_INDICATOR'].map(indic_dic)
# Creating the 'PRODUCT' column
prod_dic = dict(
zip(
EurostatNRG.INDIC_PROD['nrg110a_prod'].KEY_PRODUCT.astype(
str), EurostatNRG.INDIC_PROD['nrg110a_prod'].PRODUCT))
df['PRODUCT'] = df['KEY_PRODUCT'].map(prod_dic)
# Delete che columns 'KEY_INDICATOR' and 'KEY_PRODUCT', and
# rearrange the columns in the desired order
df.drop(columns=['KEY_INDICATOR', 'KEY_PRODUCT'], inplace=True)
main_cols = ['INDICATOR', 'PRODUCT', 'UNIT', 'COUNTRY']
year_cols = [y for y in df.columns if y not in main_cols]
cols = main_cols + year_cols
df = df.reindex(columns=cols)
# Pivot the DataFrame to have it in flat format
df = df.melt(
id_vars=df.columns[:4], var_name='YEAR', value_name='VALUE')
# Convert the 'VALUE' column into float numbers
df['VALUE'] = pd.to_numeric(df['VALUE'], downcast='float')
# Drop rows that have no indicators (it means they are not in
# the Excel file with the products of interest)
df.dropna(subset=['INDICATOR', 'PRODUCT'], inplace=True)
return df
EDIT 2: if this could help, this is the error I receive when using the EurostatNRG class in IPython:
[autoreload of EurostatNRG failed: Traceback (most recent call last):
File
"C:\Users\CAPIZZIF\AppData\Local\Continuum\anaconda3\lib\site-packages\IPython\extensions\autoreload.py",
line 244, in check
superreload(m, reload, self.old_objects) File "C:\Users\CAPIZZIF\AppData\Local\Continuum\anaconda3\lib\site-packages\IPython\extensions\autoreload.py",
line 394, in superreload
update_generic(old_obj, new_obj) File "C:\Users\CAPIZZIF\AppData\Local\Continuum\anaconda3\lib\site-packages\IPython\extensions\autoreload.py",
line 331, in update_generic
update(a, b) File "C:\Users\CAPIZZIF\AppData\Local\Continuum\anaconda3\lib\site-packages\IPython\extensions\autoreload.py",
line 279, in update_class
if (old_obj == new_obj) is True: File "C:\Users\CAPIZZIF\AppData\Local\Continuum\anaconda3\lib\site-packages\pandas\core\generic.py",
line 1478, in nonzero
.format(self.class.name)) ValueError: The truth value of a DataFrame is ambiguous. Use a.empty, a.bool(), a.item(), a.any() or
a.all(). ]
I managed to find the culprit.
In the NRG_flat method, the lines:
df['INDICATOR'] = df['KEY_INDICATOR'].map(indic_dic)
...
df['PRODUCT'] = df['KEY_PRODUCT'].map(indic_dic)
mess up the copies of the df DataFrame, thus I had to change them with the Pandas assign method:
df = df.assign(INDICATOR=df.KEY_INDICATOR.map(prod_dic))
...
df = df.assign(PRODUCT=df.KEY_PRODUCT.map(prod_dic))
I do not get any more error.
Thank you for replying!
I am having trouble figuring out how to properly transpose data in a DataFrame in order to calculate differences between actuals and targets. Doing something like: df['difference'] = df['Revenue'] - df['Target'], is straightforward so this is more a question of desired output formatting.
Assume you have a DataFrame with the follow columns and values:
Desire outputs would be a roll up from both sources and comparison at the Source level. Assume there are 30+ additional data points similar to revenue, users, and new users... :
and
Any and all suggestions are very much appreciated.
Setup
df = pd.DataFrame([
['2016-06-01', 15000, 10000, 1000, 900, 100, 50, 'US'],
['2016-06-01', 16000, 12000, 1500, 1200, 150, 100, 'UK']
], columns=['Date', 'Revenue', 'Target', 'Users', 'Target', 'New Users', 'Target', 'Source'])
df
Your columns are not unique. I'll start with moving Source and Date into the index and renaming the columns.
df1 = df.copy()
df1.Date = pd.to_datetime(df1.Date)
df1 = df1.set_index(['Date', 'Source'])
idx = pd.MultiIndex.from_product([['Revenue', 'Users', 'New Users'], ['Actual', 'Target']])
df1.columns = idx
df1
Then move the first level of columns to the index
df1 = df1.stack(0)
df1
From here, I'm going to sum sources across ['Revenue', 'Users', 'New Users'] and assign the result to df2.
df2 = df1.groupby(level=-1).sum()
df2
Finally:
df2['Difference'] = df2.Actual / df2.Target
df1['Difference'] = df1.Actual / df1.Target
df2
df1.stack().unstack([0, 1, -1])