As part of my research, I am searching a good storing design for my panel data. I am using pandas for all in-memory operations. I've had a look at the following two questions/contributions, Large Data Work flows using Pandas and Query HDF5 Pandas as they come closest to my set-up. However, I have a couple of questions left. First, let me define my data and some requirements:
Size: I have around 800 dates, 9000 IDs and up to 200 variables. Hence, flattening the panel (along dates and IDs) corresponds to 7.2mio rows and 200 columns. This might all fit in memory or not, let's assume it does not. Disk-space is not an issue.
Variables are typically calculated once, but updates/changes probably happen from time to time. Once updates occur, old versions don't matter anymore.
New variables are added from time to time, mostly one at a time only.
New rows are not added.
Querying takes place. For example, often I need to select only a certain date range like date>start_date & date<end_date. But some queries need to consider rank conditions on dates. For example, get all data (i.e. columns) where rank(var1)>500 & rank(var1)<1000, where rank is as of date.
The objective is to achieve fast reading/querying of data. Data writing is not so critical.
I thought of the following HDF5 design:
Follow the groups_map approach (of 1) to store variables in different tables. Limit the number of columns for each group to 10 (to avoid large memory loads when updating single variables, see point 3).
Each group represents one table, where I use the multi-index based on dates & ids for each table stored.
Create an update function, to update variables. The functions loads the table with all (10) columns to memory as a df, deletes the table on the disk, replaces the updated variable in df and saves the table from memory back to disk.
Create an add function, add var1 to a group with less than 10 columns, or create new group if required. Saving similar as in 3. load current group to memory, delete table on disk, add new column and save it back on disk.
Calculate ranks as of date for relevant variables and add them to disk-storage as rank_var1, which should reduce the query as of to simply rank_var1 > 500 & rank_var1<1000.
I have the following questions:
Updating HDFTable, I suppose I have to delete the entire table in order to update a single column?
When to use 'data_columns', or should I simply assign True in HDFStore.append()?
If I want to query based on condition of rank_var1 > 500 & rank_var1<1000, but I need columns from other groups. Can I enter the index received from the rank_var1 condition into the query to get other columns based on this index (the index is a multi-index with date and ID)? Or would I need to loop this index by date and then chunk the IDs similar as proposed in 2 and repeat the procedure for each group where I need. Alternatively, (a) I could add to each groups table rank columns, but it seems extremely inefficient in terms of disk-storage. Note, the number of variables where rank filtering is relevant is limited (say 5). Or (b) I could simply use the df_rank received from the rank_var1 query and use in-memory operations via df_rank.merge(df_tmp, left_index=True, right_index=True, how='left') and loop through groups (df_tmp) where I select the desired columns.
Say I have some data in different frequencies. Having different group_maps (or different storages) for different freq is the way to go I suppose?
Copies of the storage might be used on win/ux systems. I assume it is perfectly compatible, anything to consider here?
I plan to use pd.HDFStore(str(self.path), mode='a', complevel=9, complib='blosc'). Any concerns regarding complevel or complib?
I've started to write up some code, once I have something to show I'll edit and add it if desired. Please, let me know if you need any more information.
EDIT I here a first version of my storage class, please adjust path at bottom accordingly. Sorry for the length of the code, comments welcome
import pandas as pd
import numpy as np
import string
class LargeDFStorage():
# TODO add index features to ensure correct indexes
# index_names = ('date', 'id')
def __init__(self, h5_path, groups_map):
"""
Parameters
----------
h5_path: str
hdf5 storage path
groups_map: dict
where keys are group_names and values are dict, with at least key
'columns' where the value is list of column names.
A special group_name is reserved for group_name/key "query", which
can be used as queering and conditioning table when getting data,
see :meth:`.get`.
"""
self.path = str(h5_path)
self.groups_map = groups_map
self.column_map = self._get_column_map()
# if desired make part of arguments
self.complib = 'blosc'
self.complevel = 9
def _get_column_map(self):
""" Calc the inverse of the groups_map/ensures uniqueness of cols
Returns
-------
dict: with cols as keys and group_names as values
"""
column_map = dict()
for g, value in self.groups_map.items():
if len(set(column_map.keys()) & set(value['columns'])) > 0:
raise ValueError('Columns have to be unique')
for col in value['columns']:
column_map[col] = g
return column_map
#staticmethod
def group_col_names(store, group_name):
""" Returns all column names of specific group
Parameters
----------
store: pd.HDFStore
group_name: str
Returns
-------
list:
of all column names in the group
"""
if group_name not in store:
return []
# hack to get column names, straightforward way!?
return store.select(group_name, start=0, stop=0).columns.tolist()
#staticmethod
def stored_cols(store):
""" Collects all columns stored in HDF5 store
Parameters
----------
store: pd.HDFStore
Returns
-------
list:
a list of all columns currently in the store
"""
stored_cols = list()
for x in store.items():
group_name = x[0][1:]
stored_cols += LargeDFStorage.group_col_names(store, group_name)
return stored_cols
def _find_groups(self, columns):
""" Searches all groups required for covering columns
Parameters
----------
columns: list
list of valid columns
Returns
-------
list:
of unique groups
"""
groups = list()
for column in columns:
groups.append(self.column_map[column])
return list(set(groups))
def add_columns(self, df):
""" Adds columns to storage for the first time. If columns should
be updated use(use :meth:`.update` instead)
Parameters
----------
df: pandas.DataFrame
with new columns (not yet stored in any of the tables)
Returns
-------
"""
store = pd.HDFStore(self.path, mode='a' , complevel=self.complevel,
complib=self.complib)
# check if any column has been stored already
if df.columns.isin(self.stored_cols(store)).any():
store.close()
raise ValueError('Some cols are already in the store')
# find all groups needed to store the data
groups = self._find_groups(df.columns)
for group in groups:
v = self.groups_map[group]
# select columns of current group in df
select_cols = df.columns[df.columns.isin(v['columns'])].tolist()
tmp = df.reindex(columns=select_cols, copy=False)
# set data column to False only in case of query data
dc = None
if group=='query':
dc = True
stored_cols = self.group_col_names(store,group)
# no columns in group (group does not exists yet)
if len(stored_cols)==0:
store.append(group, tmp, data_columns=dc)
else:
# load current disk data to memory
df_grp = store.get(group)
# remove data from disk
store.remove(group)
# add new column(s) to df_disk
df_grp = df_grp.merge(tmp, left_index=True, right_index=True,
how='left')
# save old data with new, additional columns
store.append(group, df_grp, data_columns=dc)
store.close()
def _query_table(self, store, columns, where):
""" Selects data from table 'query' and uses where expression
Parameters
----------
store: pd.HDFStore
columns: list
desired data columns
where: str
a valid select expression
Returns
-------
"""
query_cols = self.group_col_names(store, 'query')
if len(query_cols) == 0:
store.close()
raise ValueError('No data to query table')
get_cols = list(set(query_cols) & set(columns))
if len(get_cols) == 0:
# load only one column to minimize memory usage
df_query = store.select('query', columns=query_cols[0],
where=where)
add_query = False
else:
# load columns which are anyways needed already
df_query = store.select('query', columns=get_cols, where=where)
add_query = True
return df_query, add_query
def get(self, columns, where=None):
""" Retrieve data from storage
Parameters
----------
columns: list/str
list of columns to use, or use 'all' if all columns should be
retrieved
where: str
a valid select statement
Returns
-------
pandas.DataFrame
with all requested columns and considering where
"""
store = pd.HDFStore(str(self.path), mode='r')
# get all columns in stored in HDFStorage
stored_cols = self.stored_cols(store)
if columns == 'all':
columns = stored_cols
# check if all desired columns can be found in storage
if len(set(columns) - set(stored_cols)) > 0:
store.close()
raise ValueError('Column(s): {}. not in storage'.format(
set(columns)- set(stored_cols)))
# get all relevant groups (where columns are taken from)
groups = self._find_groups(columns)
# if where query is defined retrieve data from storage, eventually
# only index of df_query might be used
if where is not None:
df_query, add_df_query = self._query_table(store, columns, where)
else:
df_query, add_df_query = None, False
# dd collector
df = list()
for group in groups:
# skip in case where was used and columns used from
if where is not None and group=='query':
continue
# all columns which are in group but also requested
get_cols = list(
set(self.group_col_names(store, group)) & set(columns))
tmp_df = store.select(group, columns=get_cols)
if df_query is None:
df.append(tmp_df)
else:
# align query index with df index from storage
df_query, tmp_df = df_query.align(tmp_df, join='left', axis=0)
df.append(tmp_df)
store.close()
# if any data of query should be added
if add_df_query:
df.append(df_query)
# combine all columns
df = pd.concat(df, axis=1)
return df
def update(self, df):
""" Updates data in storage, all columns have to be stored already in
order to be accepted for updating (use :meth:`.add_columns` instead)
Parameters
----------
df: pd.DataFrame
with index as in storage, and column as desired
Returns
-------
"""
store = pd.HDFStore(self.path, mode='a' , complevel=self.complevel,
complib=self.complib)
# check if all column have been stored already
if df.columns.isin(self.stored_cols(store)).all() is False:
store.close()
raise ValueError('Some cols have not been stored yet')
# find all groups needed to store the data
groups = self._find_groups(df.columns)
for group in groups:
dc = None
if group=='query':
dc = True
# load current disk data to memory
group_df = store.get(group)
# remove data from disk
store.remove(group)
# update with new data
group_df.update(df)
# save updated df back to disk
store.append(group, group_df, data_columns=dc)
store.close()
class DataGenerator():
np.random.seed(1282)
#staticmethod
def get_df(rows=100, cols=10, freq='M'):
""" Simulate data frame
"""
if cols < 26:
col_name = list(string.ascii_lowercase[:cols])
else:
col_name = range(cols)
if rows > 2000:
freq = 'Min'
index = pd.date_range('19870825', periods=rows, freq=freq)
df = pd.DataFrame(np.random.standard_normal((rows, cols)),
columns=col_name, index=index)
df.index.name = 'date'
df.columns.name = 'ID'
return df
#staticmethod
def get_panel(rows=1000, cols=500, items=10):
""" simulate panel data
"""
if items < 26:
item_names = list(string.ascii_lowercase[:cols])
else:
item_names = range(cols)
panel_ = dict()
for item in item_names:
panel_[item] = DataGenerator.get_df(rows=rows, cols=cols)
return pd.Panel(panel_)
def main():
# Example of with DataFrame
path = 'D:\\fc_storage.h5'
groups_map = dict(
a=dict(columns=['a', 'b', 'c', 'd', 'k']),
query=dict(columns=['e', 'f', 'g', 'rank_a']),
)
storage = LargeDFStorage(path, groups_map=groups_map)
df = DataGenerator.get_df(rows=200000, cols=15)
storage.add_columns(df[['a', 'b', 'c', 'e', 'f']])
storage.update(df[['a']]*3)
storage.add_columns(df[['d', 'g']])
print(storage.get(columns=['a','b', 'f'], where='f<0 & e<0'))
# Example with panel and rank condition
path2 = 'D:\\panel_storage.h5'
storage_pnl = LargeDFStorage(path2, groups_map=groups_map)
panel = DataGenerator.get_panel(rows=800, cols=2000, items=24)
df = panel.to_frame()
df['rank_a'] = df[['a']].groupby(level='date').rank()
storage_pnl.add_columns(df[['a', 'b', 'c', 'e', 'f']])
storage_pnl.update(df[['a']]*3)
storage_pnl.add_columns(df[['d', 'g', 'rank_a']])
print(storage_pnl.get(columns=['a','b','e', 'f', 'rank_a'],
where='f>0 & e>0 & rank_a <100'))
if __name__ == '__main__':
main()
It's bit difficult to answer those questions without particular examples...
Updating HDFTable, I suppose I have to delete the entire table in
order to update a single column?
AFAIK yes unless you are storing single columns separately, but it will be done automatically, you just have to write your DF/Panel back to HDF Store.
When to use 'data_columns', or should I simply assign True in
HDFStore.append()?
data_columns=True - will index all your columns - IMO it's waste of resources unless you are going to use all columns in the where parameter (i.e. if all columns should be indexed).
I would specify there only those columns that will be used often for searching in where= clause. Consider those columns as indexed columns in a database table.
If I want to query based on condition of rank_var1 > 500 &
rank_var1<1000, but I need columns from other groups. Can I enter the
index received from the rank_var1 condition into the query to get
other columns based on this index (the index is a multi-index with
date and ID)?
I think we would need some reproducible sample data and examples of your queries in order to give a reasonable answer...
Copies of the storage might be used on win/ux systems. I assume it is
perferctly compatible, anything to consider here?
Yes, it should be fully compatible
I plan to use pd.HDFStore(str(self.path), mode='a', complevel=9,
complib='blosc'). Any concerns regarding complevel or complib?
Test it with your data - results might depend on dtypes, number of unique values, etc. You may also want to consider lzo complib - it might be faster in some use-cases. Check this. Sometimes a high complevel doesn't give you better copression ratio, but will be slower (see results of my old comparison)
Related
I have two dataframes: one comprising a large data set, allprice_df, with time price series for all stocks; and the other, init_df, comprising selective stocks and trade entry dates. I am trying to find the highest price for each ticker symbol and its associated date.
The following code works but it is time consuming, and I am wondering if there is a better, more Pythonic way to accomplish this.
# Initial call
init_df = init_df.assign(HighestHigh = lambda x:
highestHigh(x['DateIdentified'], x['Ticker'], allprice_df))
# HighestHigh function in lambda call
def highestHigh(date1,ticker,allp_df):
if date1.size == ticker.size:
temp_df = pd.DataFrame(columns = ['DateIdentified','Ticker'])
temp_df['DateIdentified'] = date1
temp_df['Ticker'] = ticker
else:
print("dates and tickers size mismatching")
sys.exit(1)
counter = itertools.count(0)
high_list = [getHigh(x,y,allp_df, next(counter)) for x, y in zip(temp_df['DateIdentified'],temp_df['Ticker'])]
return high_list
# Getting high for each ticker
def getHigh(dateidentified,ticker,allp_df, count):
print("trade %s" % count)
currDate = datetime.datetime.now().date()
allpm_df = allp_df.loc[((allp_df['Ticker']==ticker)&(allp_df['date']>dateidentified)&(allp_df['date']<=currDate)),['high','date']]
hh = allpm_df.iloc[:,0].max()
hd = allpm_df.loc[(allpm_df['high']==hh),'date']
hh = round(hh,2)
h_list = [hh,hd]
return h_list
# Split the list in to 2 columns one with price and the other with the corresponding date
init_df = split_columns(init_df,"HighestHigh")
# The function to split the list elements in to different columns
def split_columns(orig_df,col):
split_df = pd.DataFrame(orig_df[col].tolist(),columns=[col+"Mod", col+"Date"])
split_df[col+"Date"] = split_df[col+"Date"].apply(lambda x: x.squeeze())
orig_df = pd.concat([orig_df,split_df], axis=1)
orig_df = orig_df.drop(col,axis=1)
orig_df = orig_df.rename(columns={col+"Mod": col})
return orig_df
There are a couple of obvious solutions that would help reduce your runtime.
First, in your getHigh function, instead of using loc to get the date associated with the maximum value for high, use idxmax to get the index of the row associated with the high and then access that row:
hh, hd = allpm_df[allpm_df['high'].idxmax()]
This will replace two O(N) operations (finding the maximum in a list, and doing a list lookup using a comparison) with one O(N) operation and one O(1) operation.
Edit
In light of your information on the size of your dataframes, my best guess is that this line is probably where most of your time is being consumed:
allpm_df = allp_df.loc[((allp_df['Ticker']==ticker)&(allp_df['date']>dateidentified)&(allp_df['date']<=currDate)),['high','date']]
In order to make this faster, I would setup your data frame to include a multi-index when you first create the data frame:
index = pd.MultiIndex.from_arrays(arrays = [ticker_symbols, dates], names = ['Symbol', 'Date'])
allp_df = pd.Dataframe(data, index = index)
allp_df.index.sortlevel(level = 0, sort_remaining = True)
This should create a dataframe with a sorted, multi-level index associated with your ticker symbol and date. Doing this will reduce your search time tremendously. Once you do that, you should be able to access all the data associated with a ticker symbol and a given date-range by doing this:
allp_df[ticker, (dateidentified: currDate)]
which should return your data much more quickly. For more information on multi-indexing, check out this helpful Pandas tutorial.
So I have a dataframe called reactions_drugs
and I want to create a table called new_r_d where I keep track of how often a see a symptom for a given medication like
Here is the code I have but I am running into errors such as "Unable to coerce to Series, length must be 3 given 0"
new_r_d = pd.DataFrame(columns = ['drugname', 'reaction', 'count']
for i in range(len(reactions_drugs)):
name = reactions_drugs.drugname[i]
drug_rec_act = reactions_drugs.drug_rec_act[i]
for rec in drug_rec_act:
row = new_r_d.loc[(new_r_d['drugname'] == name) & (new_r_d['reaction'] == rec)]
if row == []:
# create new row
new_r_d.append({'drugname': name, 'reaction': rec, 'count': 1})
else:
new_r_d.at[row,'count'] += 1
Assuming the rows in your current reactions (drug_rec_act) column contain one string enclosed in a list, you can convert the values in that column to lists of strings (by splitting each string on the comma delimiter) and then utilize the explode() function and value_counts() to get your desired result:
df['drug_rec_act'] = df['drug_rec_act'].apply(lambda x: x[0].split(','))
df_long = df.explode('drug_rec_act')
result = df_long.groupby('drugname')['drug_rec_act'].value_counts().reset_index(name='count')
The code below maps values and column names of my reference df with my actual dataset, finding exact matches and if an exact match is found, return the OutputValue. However, I'm trying to add the rule that when PrimaryValue = DEFAULT to also return the OutputValue.
The solution I'm trying out to tackle this is to create a new dataframe with null values - since there was no match provided by code below. Thus the next step would be to target the null values whose corresponding PrimaryValue = DEFAULT to replace null by the OutputValue.
#create a map based on columns from reference_df
map_key = concat_ws('\0', final_reference.PrimaryName, final_reference.PrimaryValue)
map_value = final_reference.OutputValue
#dataframe of concatinated mappings to get the corresponding OutputValues from reference table
d = final_reference.agg(collect_set(array(concat_ws('\0','PrimaryName','PrimaryValue'), 'OutputValue')).alias('m')).first().m
#display(d)
#iterate through mapped values
mappings = create_map([lit(i) for i in chain.from_iterable(d)])
#dataframe with corresponding matched OutputValues
dataset = datasetM.select("*",*[ mappings[concat_ws('\0', lit(c), col(c))].alias(c_name) for c,c_name in matched_List.items()])
display(dataset)
From discussion in comments, I think you just need to add a default mappings from the existing one and then use coalease() function to find the first non-null value, see below:
from pyspark.sql.functions import collect_set, array, concat_ws, lit, col, create_map, coalesce
# skip some old code
d
#[['LeaseStatus\x00Abandoned', 'Active'],
# ['LeaseStatus\x00DEFAULT', 'Pending'],
# ['LeaseRecoveryType\x00Gross-modified', 'Modified Gross'],
# ['LeaseStatus\x00Archive', 'Expired'],
# ['LeaseStatus\x00Terminated', 'Terminated'],
# ['LeaseRecoveryType\x00Gross w/base year', 'Modified Gross'],
# ['LeaseRecoveryType\x00Gross', 'Gross']]
# original mapping
mappings = create_map([ lit(j) for i in d for j in i ])
# default mapping
mappings_default = create_map([ lit(j.split('\0')[0]) for i in d if i[0].upper().endswith('\x00DEFAULT') for j in i ])
#Column<b'map(LeaseStatus, Pending)'>
# a set of available PrimaryLookupAttributeName
available_list = set([ i[0].split('\0')[0] for i in d ])
# {'LeaseRecoveryType', 'LeaseStatus'}
# use coalesce to find the first non-null values from mappings, mappings_defaul etc
datasetPrimaryAttributes_False = datasetMatchedPortfolio.select("*",*[
coalesce(
mappings[concat_ws('\0', lit(c), col(c))],
mappings_default[c],
lit("Not Specified at Source" if c in available_list else "Lookup not found")
).alias(c_name) for c,c_name in matchedAttributeName_List.items()])
Some explanation:
(1) d is a list of lists retrieved from the reference_df, we use a list comprehension [ lit(j) for i in d for j in i ] to flatten this to a list and apply the flattened list to the create_map function:
(2) The mappings_default is similar to the above, but add a if condition to serve as a filter and keep only entries having PrimaryLookupAttributeValue (which is the first item of the inner list i[0]) ending with \x00DEFAULT and then use split to strip PrimaryLookupAttributeValue(which is basically \x00DEFAULT) off from the map_key.
I'm new to any kind of programming as you can tell by this 'beautiful' piece of hard coding. With sweat and tears (not so bad, just a little), I've created a very sequential code and that's actually my problem. My goal is to create a somewhat-automated script - probably including for-loop (I've unsuccessfully tried).
The main aim is to create a randomization loop which takes original dataset looking like this:
dataset
From this data set picking randomly row by row and saving it one by one to another excel list. The point is that the row from columns called position01 and position02 should be always selected so it does not match with the previous pick in either of those two column values. That should eventually create an excel sheet with randomized rows that are followed always by a row that does not include values from the previous pick. So row02 should not include any of those values in columns position01 and position02 of the row01, row3 should not contain values of the row2, etc. It should also iterate in the range of the list length, which is 0-11. Important is also the excel output since I need the rest of the columns, I just need to shuffle the order.
I hope my aim and description are clear enough, if not, happy to answer any questions. I would appreciate any hint or help, that helps me 'unstuck'. Thank you. Code below. (PS: I'm aware of the fact that there is probably much more neat solution to it than this)
import pandas as pd
import random
dataset = pd.read_excel("C:\\Users\\ibm\\Documents\\Psychopy\\DataInput_Training01.xlsx")
# original data set use for comparisons
imageDataset = dataset.loc[0:11, :]
# creating empty df for storing rows from imageDataset
emptyExcel = pd.DataFrame()
randomPick = imageDataset.sample() # select randomly one row from imageDataset
emptyExcel = emptyExcel.append(randomPick) # append a row to empty df
randomPickIndex = randomPick.index.tolist() # get index of the row
imageDataset2 = imageDataset.drop(index=randomPickIndex) # delete the row with index selected before
# getting raw values from the row 'position01'/02 are columns headers
randomPickTemp1 = randomPick['position01'].values[0]
randomPickTemp2 = randomPick
randomPickTemp2 = randomPickTemp2['position02'].values[0]
# getting a dataset which not including row values from position01 and position02
isit = imageDataset2[(imageDataset2.position01 != randomPickTemp1) & (imageDataset2.position02 != randomPickTemp1) & (imageDataset2.position01 != randomPickTemp2) & (imageDataset2.position02 != randomPickTemp2)]
# pick another row from dataset not including row selected at the beginning - randomPick
randomPick2 = isit.sample()
# save it in empty df
emptyExcel = emptyExcel.append(randomPick2, sort=False)
# get index of this second row to delete it in next step
randomPick2Index = randomPick2.index.tolist()
# delete the another row
imageDataset3 = imageDataset2.drop(index=randomPick2Index)
# AND REPEAT the procedure of comparison of the raw values with dataset already not including the original row:
randomPickTemp1 = randomPick2['position01'].values[0]
randomPickTemp2 = randomPick2
randomPickTemp2 = randomPickTemp2['position02'].values[0]
isit2 = imageDataset3[(imageDataset3.position01 != randomPickTemp1) & (imageDataset3.position02 != randomPickTemp1) & (imageDataset3.position01 != randomPickTemp2) & (imageDataset3.position02 != randomPickTemp2)]
# AND REPEAT with another pick - save - matching - picking again.. until end of the length of the dataset (which is 0-11)
So at the end I've used a solution provided by David Bridges (post from Sep 19 2019) on psychopy websites. In case anyone is interested, here is a link: https://discourse.psychopy.org/t/how-do-i-make-selective-no-consecutive-trials/9186
I've just adjusted the condition in for loop to my case like this:
remaining = [choices[x] for x in choices if last['position01'] != choices[x]['position01'] and last['position01'] != choices[x]['position02'] and last['position02'] != choices[x]['position01'] and last['position02'] != choices[x]['position02']]
Thank you very much for the helpful answer! and hopefully I did not spam it over here too much.
import itertools as it
import random
import pandas as pd
# list of pair of numbers
tmp1 = [x for x in it.permutations(list(range(6)),2)]
df = pd.DataFrame(tmp1, columns=["position01","position02"])
df1 = pd.DataFrame()
i = random.choice(df.index)
df1 = df1.append(df.loc[i],ignore_index = True)
df = df.drop(index = i)
while not df.empty:
val = list(df1.iloc[-1])
tmp = df[(df["position01"]!=val[0])&(df["position01"]!=val[1])&(df["position02"]!=val[0])&(df["position02"]!=val[1])]
if tmp.empty: #looped for 10000 times, was never empty
print("here")
break
i = random.choice(tmp.index)
df1 = df1.append(df.loc[i],ignore_index = True)
df = df.drop(index=i)
First time posting here, I apologize if this question has been asked before - I can't find anything that applies.
Is there a way to read the underlying data from an Excel PivotTable into a Pandas Data Frame? For several years I've had an Excel Auto_Open macro that downloads several Excel files and double clicks on the "Grand Total" row in order to extract all of the data, which ultimate gets imported into a database. This is done because the owners of the source data refuse to grant access to the database itself.
This macro has never been the ideal scenario and we need to move it to a better method soon. I have extensive SQL knowledge but have only recently begun to learn Python.
I have been able to read worksheets using OpenPyXl, but these files do not contain the source data on a separate worksheet by default - the pivotcache must be extracted to a new sheet first. What I would like to do, if possible, is read from the Excel PivotCache into a Pandas Data Frame and either save that output as a CSV or load it directly into our database. It seems that this is not capable with OpenPyXl and that I'll probably need to use win32com.client.
Does anybody have any experience with this, and know if it's even possible? Any pointers for where I might get started? I've tried several items from the Excel Object model (PivotCache, GetData, etc etc) but either I don't know how to use them or they don't return what I need.
Any help would be much appreciated. Thanks!
This answer is very late, but I came up with it while struggling with the same issue, and some of the comments above helped me nail it.
In essence, the steps one can take to solve this with openpyxl are:
Use openpyxl to get the openpyxl.pivot.table.TableDefinition object from the desired pivot table (let's call it my_pivot_table)
Get cached fields and their values from my_pivot_table.cache.cacheFields
Get rows data as a dict in two sub-steps:
3.1) Get all cached rows and their values from my_pivot_table.cache.records.r. Cache fields in these records are stored as indexes from my_pivot_table.cache.cacheFields
3.2) Replace cache fields from each record by their actual values, by "joining" cache.records.r and cache.cacheFields
Convert dict with rows into a pandas DataFrame
Below you will find a copy of the code that implements such solution. Since the structure of these Excel objects are somewhat complex, the code will probably look very cryptic (sorry about that). To address this, I'm adding further below minimal examples of the main objects being manipulated, so people can get a better sense of what is going on, what are the objects being returned, etc.
This was the simplest approach I could find to achieve this. I hope it is still useful for someone, albeit some tweaking may be needed for individual cases.
"Bare" code
import numpy as np
import pandas as pd
from openpyxl import load_workbook
from openpyxl.pivot.fields import Missing
file_path = 'path/to/your/file.xlsx'
workbook = load_workbook(file_path)
worksheet = workbook['Plan1']
# Name of desired pivot table (the same name that appears within Excel)
pivot_name = 'Tabela dinâmica1'
# Extract the pivot table object from the worksheet
pivot_table = [p for p in worksheet._pivots if p.name == pivot_name][0]
# Extract a dict of all cache fields and their respective values
fields_map = {}
for field in pivot_table.cache.cacheFields:
if field.sharedItems.count > 0:
fields_map[field.name] = [f.v for f in field.sharedItems._fields]
# Extract all rows from cache records. Each row is initially parsed as a dict
column_names = [field.name for field in pivot_table.cache.cacheFields]
rows = []
for record in pivot_table.cache.records.r:
# If some field in the record in missing, we replace it by NaN
record_values = [
field.v if not isinstance(field, Missing) else np.nan for field in record._fields
]
row_dict = {k: v for k, v in zip(column_names, record_values)}
# Shared fields are mapped as an Index, so we replace the field index by its value
for key in fields_map:
row_dict[key] = fields_map[key][row_dict[key]]
rows.append(row_dict)
df = pd.DataFrame.from_dict(rows)
Results:
>>> df.head(2)
FUEL YEAR REGION STATE UNIT Jan Feb (...)
0 GASOLINE (m3) 2000.0 S TEXAS m3 9563.263 9563.263 (...)
1 GASOLINE (m3) 2000.0 NE NEW YORK m3 3065.758 9563.263 (...)
Some of the objects details
Object pivot_table
This is an object of type openpyxl.pivot.table.TableDefinition. It is quite complex. A small glimpse of it:
<openpyxl.pivot.table.TableDefinition object>
Parameters:
name='Tabela dinâmica1', cacheId=36, dataOnRows=True, dataPosition=None, (A LOT OF OMITTED STUFF...)
Parameters:
ref='B52:W66', firstHeaderRow=1, firstDataRow=2, firstDataCol=1, rowPageCount=2, colPageCount=1, pivotFields=[<openpyxl.pivot.table.PivotField object>
Parameters: (A LOT OF OMITTED STUFF...)
Object fields_map (from cache.cacheFields)
This is a dict with column name and their available values:
{'YEAR': [2000.0, 2001.0, 2002.0, 2003.0, 2004.0, 2005.0, 2006.0, 2007.0, 2008.0,
2009.0, 2010.0, 2011.0, 2012.0, 2013.0, 2014.0, 2015.0, 2016.0, 2017.0,
2018.0, 2019.0, 2020.0],
'FUEL': ['GASOLINE (m3)', 'AVIATION GASOLINE (m3)', 'KEROSENE (m3)'],
'STATE': ['TEXAS', 'NEW YORK', 'MAINE', (...)],
'REGION': ['S', 'NE', 'N', (...)]}
Object row_dict (before mapping)
Each row is a dict with column names and their values. Raw values for cache fields are not stored here. Here they are represented by their indexes in cache.cacheFields (see above)
{'YEAR': 0, # <<<--- 0 stands for index in fields_map
'Jan': 10719.983,
'Feb': 12482.281,
'FUEL': 0, # <<<--- index in fields_map
'Dec': 10818.094,
'STATE': 0, # <<<--- index in fields_map
(...)
'UNIT': 'm3'}
Object row_dict (after mapping)
After extracting raw values for cache fields from their indexes, we have a dict that represent all values of a row:
{'YEAR': 2000.0, # extracted column value from index in fields_map
'Jan': 10719.983,
'Feb': 12482.281,
'FUEL': 'GASOLINE (m3)', # extracted from fields_map
'Dec': 10818.094,
'STATE': 'TEXAS', # extracted from fields_map
(...)
'UNIT': 'm3'}
Building on #PMHM excellent answer I have modified the code to take care of source data with blank cells. The piece of code that needed modification is the following:
for field in pivot_table.cache.cacheFields:
if field.sharedItems.count > 0:
# take care of cases where f.v returns an AttributeError because the cell is empty
# fields_map[field.name] = [f.v for f in field.sharedItems._fields]
l = []
for f in field.sharedItems._fields:
try:
l += [f.v]
except AttributeError:
l += [""]
fields_map[field.name] = l
The complete code (mostly copy/paste from above) is therefore:
import numpy as np
import pandas as pd
from openpyxl import load_workbook
from openpyxl.pivot.fields import Missing
file_path = 'path/to/your/file.xlsx'
workbook = load_workbook(file_path)
worksheet = workbook['Plan1']
# Name of desired pivot table (the same name that appears within Excel)
pivot_name = 'Tabela dinâmica1'
# Extract the pivot table object from the worksheet
pivot_table = [p for p in worksheet._pivots if p.name == pivot_name][0]
# Extract a dict of all cache fields and their respective values
fields_map = {}
for field in pivot_table.cache.cacheFields:
if field.sharedItems.count > 0:
# take care of cases where f.v returns an AttributeError because the cell is empty
# fields_map[field.name] = [f.v for f in field.sharedItems._fields]
l = []
for f in field.sharedItems._fields:
try:
l += [f.v]
except AttributeError:
l += [""]
fields_map[field.name] = l
# Extract all rows from cache records. Each row is initially parsed as a dict
column_names = [field.name for field in pivot_table.cache.cacheFields]
rows = []
for record in pivot_table.cache.records.r:
# If some field in the record in missing, we replace it by NaN
record_values = [
field.v if not isinstance(field, Missing) else np.nan for field in record._fields
]
row_dict = {k: v for k, v in zip(column_names, record_values)}
# Shared fields are mapped as an Index, so we replace the field index by its value
for key in fields_map:
row_dict[key] = fields_map[key][row_dict[key]]
rows.append(row_dict)
df = pd.DataFrame.from_dict(rows)