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')
import csv
def statistics():
statistics2 = {}
with open("BLS_private.csv") as f:
reader = csv.reader(f)
for row in reader:
statistics2 = row
return statistics2
statistics()
Dicionary sample data:
['2005', '110718', '110949', '111094', '111440', '111583', '111844', '112124', '112311', '112395', '112491', '112795', '112935']
['2006', '113250', '113535', '113793', '113958', '113965', '114045', '114203', '114348', '114434', '114439', '114628', '114794']
How would I go about adding all of the values together in a row except for the first value in a dictionary?
The first value is always the year; like in the sample data i have 2005 and 2006. I don't need to add the year.
Then I want to add together all of the values after that in the row. How would I do that?
(I also have a lot of years)
Welcome to StackOverflow :)
You can achieve this by utilizing list.pop(index) to grab the first item in the list as the key, and list comprehension to calculate the sums of the remaining values.
## Assign variable for dictionary ##
dictionary = {}
## Data assuming it is formatted as lists within a list ##
data = [['2005', '110718', '110949', '111094', '111440', '111583', '111844', '112124', '112311', '112395', '112491', '112795', '112935'],
['2006', '113250', '113535', '113793', '113958', '113965', '114045', '114203', '114348', '114434', '114439', '114628', '114794']]
## Iterate over all lists within your data list
for i in data:
## Utilize list.pop(index) to grab and remove the first item in the list (Index 0)
key = i.pop(0)
## Create a key in the dictonary using the value we popped off the list
## Sum all values left in the list as an integer if it is a digit
dictionary[key] = sum([int(v) for v in i if v.isdigit()])
## Result
dictionary
{'2005': 1342679, '2006': 1369392}
I have imported an excel file and made it into a DataFrame and iterated over a column called "Titles" to spit out titles with certain keywords. I have the list of titles as "match_titles." What I want to do now is to create a For Loop to return the column before "titles" for each title in match_titles." I'm not sure why the code is not working. Any help would be appreciated.
import pandas as pd
data = pd.read_excel(r'C:\Users\bryanmccormack\Downloads\asin_list.xlsx')
df = pd.DataFrame(data, columns=['Track','Asin','Title'])
excludes = ["Chainsaw", "Diaper pail", "Leaf Blower"]
my_excludes = [set(key_word.lower().split()) for key_word in excludes]
match_titles = [e for e in df.Title if
any(keywords.issubset(e.lower().split()) for keywords in my_excludes)]
a = []
for i in match_titles:
a.append(df['Asin'])
print(a)
In your for loop you are appending the unfiltered column df['Asin'] to your list a as many times as there are values in match_titles. But there isn't any filtering of df.
One solution would be to make a column of the match_values then you can return the column Asin after filtering on that match_values column:
# make a function to perform your match analysis.
def is_match(title, excludes=["Chainsaw", "Diaper pail", "Leaf Blower"]):
my_excludes = [set(key_word.lower().split()) for key_word in excludes]
if any(keywords.issubset(title.lower().split()) for keywords in my_excludes):
return True
return False
# Make a new boolean column for the matches. This applies your
# function to each value in df['Title'] and puts the output in
# the new column.
df['match_titles'] = df['Title'].apply(is_match)
# Filter the df to only matches and return the column you want.
# Because the match_titles column is boolean it can be used as
# an index.
result = df[df['match_titles']]['Asin']
I'm trying to find out averages and standard deviation of multiple columns of my dataset and then save them as a new column in a new dataframe. i.e. for every 'GROUP' in the dataset, I want one columns in the new dataframe with its average and SD. I came up with the following script but I'm not able to name it dynamically.
Average_F1_S_list, Average_F1_M_list, SD_F1_S_list, SD_F1_M_list = ([] for i in range(4))
Groups= DF['GROUP'].unique().tolist()
for key in Groups:
Average_F1_S = DF_DICT[key]['F1_S'].mean()
Average_F1_S_list.append(Average_F1_S)
SD_F1_S = DF_DICT[key]['F1_S'].std()
SD_F1_S_list.append(SD_F1_S)
Average_F1_M = DF_DICT[key]['F1_M'].mean()
Average_F1_M_list.append(Average_F1_M)
SD_F1_M = DF_DICT[key]['F1_M'].std()
SD_F1_M_list.append(SD_F1_M)
df=pd.DataFrame({'Group':Groups,
'Average_F1_S':Average_F1_S_list,'Standard_Dev_F1_S':SD_F1_S_list,
'Average_F1_M':Average_F1_M_list,'Standard_Dev_F1_M':SD_F1_M_list},
columns=['Group','Average_F1_S','Standard_Dev_F1_S','Average_F1_M', 'Standard_Dev_F1_M'])
This will not be a good solution as there are too many features. Is there any way I can create the lists dynamically?
This should do the trick! Hope this helps
# These are all the keys you want
key_names = ['F1_S', 'F1_M']
# Holds the data you want to pass to the dataframe.
df_info = {'Groups': Groups}
for group_name in Groups:
# For each group in the groups, we iterate over all the keys we want.
for key in key_names:
# Generate a keyname that you want for your dataframe.
avg_key_name = key + '_Average'
std_key_name = key + '_Standard_Dev'
if avg_key_name not in df_info:
df_info[avg_key_name] = []
df_info[std_key_name] = []
df_info[avg_key_name].append(DF_DICT[group_name][key].mean())
df_info[std_key_name].append(DF_DICT[group_name][key].std())
df = pd.DataFrame(df_info)
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)