I am trying to create a dataframe in which i have Timestamps as index but it throws error. I can use the same methodology to create a dataframe in case the index is not a Timestamp. The following codebit is a bare min example:
Works fine:
pd.DataFrame.from_dict({'1':{'a':1,'b':2,'c':3},'2':{'a':1,'c':4},'3':{'b':6}})
output:
1 2 3
a 1 1.0 NaN
b 2 NaN 6.0
c 3 4.0 NaN
BREAKS
o=np.arange(np.datetime64('2017-11-01 00:00:00'),np.datetime64('2017-11-01 00:00:00')+np.timedelta64(3,'D'),np.timedelta64(1,'D'))
pd.DataFrame.from_records({o[0]:{'a':1,'b':2,'c':3},o[1]:{'a':1,'c':4},o[2]:{'b':6}})
output:
KeyError Traceback (most recent call last)
<ipython-input-627-f9a075f611c0> in <module>
1 o=np.arange(np.datetime64('2017-11-01 00:00:00'),np.datetime64('2017-11-01 00:00:00')+np.timedelta64(3,'D'),np.timedelta64(1,'D'))
2
----> 3 pd.DataFrame.from_records({o[0]:{'a':1,'b':2,'c':3},o[1]:{'a':1,'c':4},o[2]:{'b':6}})
~/anaconda3/envs/dfs/lib/python3.6/site-packages/pandas/core/frame.py in from_records(cls, data, index, exclude, columns, coerce_float, nrows)
1617 if columns is None:
1618 columns = arr_columns = ensure_index(sorted(data))
-> 1619 arrays = [data[k] for k in columns]
1620 else:
1621 arrays = []
~/anaconda3/envs/dfs/lib/python3.6/site-packages/pandas/core/frame.py in <listcomp>(.0)
1617 if columns is None:
1618 columns = arr_columns = ensure_index(sorted(data))
-> 1619 arrays = [data[k] for k in columns]
1620 else:
1621 arrays = []
KeyError: Timestamp('2017-11-01 00:00:00')
Please help me understand the behavior and what I am missing. Also, how do go about creating a dataframe from records that has Timestamps as indices
Change from_records to from_dict (just like in your working example)
and everything executes fine.
Another, optional hint: Since you create a Pandas DataFrame, use
pandasonic native way to create datetime values to use as column names:
o = pd.date_range(start='2017-11-01', periods=3)
Edit
I noticed that if you create o object the way I proposed (as a
date_range), you can use even from_records.
Edit 2
You wrote that you want datetime objects as the index, whereas
your code attempts to set them as column names.
If you want datetime objects as the index, run something like:
df = pd.DataFrame.from_records({'1':{o[0]:1, o[1]:2, o[2]:3},
'2':{o[0]:1, o[2]:4}, '3':{o[1]:6}})
The result is:
1 2 3
2017-11-01 1 1.0 NaN
2017-11-02 2 NaN 6.0
2017-11-03 3 4.0 NaN
Another way to create the above result is:
df = pd.DataFrame.from_records([{'1':1, '2':1}, {'1':2, '3':6}, {'1':3, '2':4}], index=o)
I am having the below table where I want to remove these rows with NaN values.
date Open ... Real Lower Band Real Upper Band
0 2020-07-08 08:05:00 2.1200 ... NaN NaN
1 2020-07-08 09:00:00 2.1400 ... NaN NaN
2 2020-07-08 09:30:00 2.1800 ... NaN NaN
3 2020-07-08 09:35:00 2.2000 ... NaN NaN
4 2020-07-08 09:40:00 2.1710 ... NaN NaN
5 2020-07-08 09:45:00 2.1550 ... NaN NaN
These NaN values are til row no. 58
For this, I wrote the following code. But the above error occurred.
data.drop(data[:59,:],inplace= True)
print(data)
Please help me!
There are many options to choose from:
Drop rows by index label.
df.drop(list(range(59)), axis=0, inplace=True)
Drop if nans in selected columns.
df.dropna(axis=0, subset=['Real Upper Band'], inplace=True)
Select rows to keep by index label slice
df = df.loc[59:, :] # 59 is the label in index, if index was date then replace 59 with corresponding datetime
Select rows to keep by integer index slice (similar to slicing a list)
df = df.iloc[59:, :] # 59 is the 0-index row number, regardless of what index is set on df
Filter with .loc and boolean array returned by .isna()
df = df.loc[~df['Real Upper Band'].isna(), :]
Remember that loc and iloc work with two dimensions when applied to dataframes, it is recomended to use full slice : to avoid ambiguity and improve performance according to the docs https://pandas.pydata.org/docs/user_guide/indexing.html
You want to keep rows from 59-th on, so the shortest code you can run is:
data = data[59:]
according to the Pandas documentation, pandas.read_csv (https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.read_csv.html) should support detection of bad lines though error_bad_lines and warn_bad_lines set to True.
So I have created a csv with bad format called test.csv:
aaa,bbb,ccc
ssdf,sdtf,aesrt,,,,
eart,erate
aert,aert,aert
and run read_csv:
>>> pd.read_csv('test.csv', error_bad_lines = True )
aaa bbb ccc
ssdf sdtf aesrt NaN NaN NaN NaN
eart erate NaN NaN NaN NaN NaN
As I understand the documentation, this should raise an Error, except it doesn't. Instead bad csv is loaded. Pandas seems to ignore all of error_bad_lines / warn_bad_lines.
Is my understanding of the documentation wrong, or is it really a bug in Pandas? Does anyone know of elegant work-around how to load only correct csv?
I'm working with Python 3.6.8, Pandas 0.25.0 and Ubuntu 18.04.
I made some tests and found that the second line will determine the number of columns to be expected in the rest of the file.
for example, the second line (ssdf,sdtf,aesrt,,,,) has 7 columns. So if all the following rows have less than 7 columns, then no errors!
If you modify one row to be 7 or more, then it will crash. The default value for error_bad_lines is true, so you don't need to specify it explicitly.
Example without an error :
data.csv :
0,jjjjjj
1,er,ate,, # 5 columns
2,bb,b
3,sdtf,aesrt,ll,sdfd # 5 columns, so no errors appear.
4,erate,
5,aert,aert
df1 = pd.read_csv('data.csv')
df1
Result : No error
0 jjjjjj
1 er ate NaN NaN
2 bb b NaN NaN
3 sdtf aesrt ll sdfd
4 erate NaN NaN NaN
5 aert aert NaN NaN
Example with an error :
data.csv :
0,jjjjjj
1,er,ate,, # 5 columns
2,bb,b
3,sdtf,aesrt,ll,sdfd,sdf,sdf,sdf,sdf, # more than 5 columns
4,erate,
5,aert,aert
df1 = pd.read_csv('data.csv')
df1
Result : error !!
ParserError: Error tokenizing data. C error: Expected 5 fields in line 4, saw 10
I have a very large dataframe (around 1 million rows) with data from an experiment (60 respondents).
I would like to split the dataframe into 60 dataframes (a dataframe for each participant).
In the dataframe, data, there is a variable called 'name', which is the unique code for each participant.
I have tried the following, but nothing happens (or execution does not stop within an hour). What I intend to do is to split the data into smaller dataframes, and append these to a list (datalist):
import pandas as pd
def splitframe(data, name='name'):
n = data[name][0]
df = pd.DataFrame(columns=data.columns)
datalist = []
for i in range(len(data)):
if data[name][i] == n:
df = df.append(data.iloc[i])
else:
datalist.append(df)
df = pd.DataFrame(columns=data.columns)
n = data[name][i]
df = df.append(data.iloc[i])
return datalist
I do not get an error message, the script just seems to run forever!
Is there a smart way to do it?
Can I ask why not just do it by slicing the data frame. Something like
#create some data with Names column
data = pd.DataFrame({'Names': ['Joe', 'John', 'Jasper', 'Jez'] *4, 'Ob1' : np.random.rand(16), 'Ob2' : np.random.rand(16)})
#create unique list of names
UniqueNames = data.Names.unique()
#create a data frame dictionary to store your data frames
DataFrameDict = {elem : pd.DataFrame() for elem in UniqueNames}
for key in DataFrameDict.keys():
DataFrameDict[key] = data[:][data.Names == key]
Hey presto you have a dictionary of data frames just as (I think) you want them. Need to access one? Just enter
DataFrameDict['Joe']
Firstly your approach is inefficient because the appending to the list on a row by basis will be slow as it has to periodically grow the list when there is insufficient space for the new entry, list comprehensions are better in this respect as the size is determined up front and allocated once.
However, I think fundamentally your approach is a little wasteful as you have a dataframe already so why create a new one for each of these users?
I would sort the dataframe by column 'name', set the index to be this and if required not drop the column.
Then generate a list of all the unique entries and then you can perform a lookup using these entries and crucially if you only querying the data, use the selection criteria to return a view on the dataframe without incurring a costly data copy.
Use pandas.DataFrame.sort_values and pandas.DataFrame.set_index:
# sort the dataframe
df.sort_values(by='name', axis=1, inplace=True)
# set the index to be this and don't drop
df.set_index(keys=['name'], drop=False,inplace=True)
# get a list of names
names=df['name'].unique().tolist()
# now we can perform a lookup on a 'view' of the dataframe
joe = df.loc[df.name=='joe']
# now you can query all 'joes'
You can convert groupby object to tuples and then to dict:
df = pd.DataFrame({'Name':list('aabbef'),
'A':[4,5,4,5,5,4],
'B':[7,8,9,4,2,3],
'C':[1,3,5,7,1,0]}, columns = ['Name','A','B','C'])
print (df)
Name A B C
0 a 4 7 1
1 a 5 8 3
2 b 4 9 5
3 b 5 4 7
4 e 5 2 1
5 f 4 3 0
d = dict(tuple(df.groupby('Name')))
print (d)
{'b': Name A B C
2 b 4 9 5
3 b 5 4 7, 'e': Name A B C
4 e 5 2 1, 'a': Name A B C
0 a 4 7 1
1 a 5 8 3, 'f': Name A B C
5 f 4 3 0}
print (d['a'])
Name A B C
0 a 4 7 1
1 a 5 8 3
It is not recommended, but possible create DataFrames by groups:
for i, g in df.groupby('Name'):
globals()['df_' + str(i)] = g
print (df_a)
Name A B C
0 a 4 7 1
1 a 5 8 3
Easy:
[v for k, v in df.groupby('name')]
Groupby can helps you:
grouped = data.groupby(['name'])
Then you can work with each group like with a dataframe for each participant. And DataFrameGroupBy object methods such as (apply, transform, aggregate, head, first, last) return a DataFrame object.
Or you can make list from grouped and get all DataFrame's by index:
l_grouped = list(grouped)
l_grouped[0][1] - DataFrame for first group with first name.
In addition to Gusev Slava's answer, you might want to use groupby's groups:
{key: df.loc[value] for key, value in df.groupby("name").groups.items()}
This will yield a dictionary with the keys you have grouped by, pointing to the corresponding partitions. The advantage is that the keys are maintained and don't vanish in the list index.
The method in the OP works, but isn't efficient. It may have seemed to run forever, because the dataset was long.
Use .groupby on the 'method' column, and create a dict of DataFrames with unique 'method' values as the keys, with a dict-comprehension.
.groupby returns a groupby object, that contains information about the groups, where g is the unique value in 'method' for each group, and d is the DataFrame for that group.
The value of each key in df_dict, will be a DataFrame, which can be accessed in the standard way, df_dict['key'].
The original question wanted a list of DataFrames, which can be done with a list-comprehension
df_list = [d for _, d in df.groupby('method')]
import pandas as pd
import seaborn as sns # for test dataset
# load data for example
df = sns.load_dataset('planets')
# display(df.head())
method number orbital_period mass distance year
0 Radial Velocity 1 269.300 7.10 77.40 2006
1 Radial Velocity 1 874.774 2.21 56.95 2008
2 Radial Velocity 1 763.000 2.60 19.84 2011
3 Radial Velocity 1 326.030 19.40 110.62 2007
4 Radial Velocity 1 516.220 10.50 119.47 2009
# Using a dict-comprehension, the unique 'method' value will be the key
df_dict = {g: d for g, d in df.groupby('method')}
print(df_dict.keys())
[out]:
dict_keys(['Astrometry', 'Eclipse Timing Variations', 'Imaging', 'Microlensing', 'Orbital Brightness Modulation', 'Pulsar Timing', 'Pulsation Timing Variations', 'Radial Velocity', 'Transit', 'Transit Timing Variations'])
# or a specific name for the key, using enumerate (e.g. df1, df2, etc.)
df_dict = {f'df{i}': d for i, (g, d) in enumerate(df.groupby('method'))}
print(df_dict.keys())
[out]:
dict_keys(['df0', 'df1', 'df2', 'df3', 'df4', 'df5', 'df6', 'df7', 'df8', 'df9'])
df_dict['df1].head(3) or df_dict['Astrometry'].head(3)
There are only 2 in this group
method number orbital_period mass distance year
113 Astrometry 1 246.36 NaN 20.77 2013
537 Astrometry 1 1016.00 NaN 14.98 2010
df_dict['df2].head(3) or df_dict['Eclipse Timing Variations'].head(3)
method number orbital_period mass distance year
32 Eclipse Timing Variations 1 10220.0 6.05 NaN 2009
37 Eclipse Timing Variations 2 5767.0 NaN 130.72 2008
38 Eclipse Timing Variations 2 3321.0 NaN 130.72 2008
df_dict['df3].head(3) or df_dict['Imaging'].head(3)
method number orbital_period mass distance year
29 Imaging 1 NaN NaN 45.52 2005
30 Imaging 1 NaN NaN 165.00 2007
31 Imaging 1 NaN NaN 140.00 2004
For more information about the seaborn datasets
NASA Exoplanets
Alternatively
This is a manual method to create separate DataFrames using pandas: Boolean Indexing
This is similar to the accepted answer, but .loc is not required.
This is an acceptable method for creating a couple extra DataFrames.
The pythonic way to create multiple objects, is by placing them in a container (e.g. dict, list, generator, etc.), as shown above.
df1 = df[df.method == 'Astrometry']
df2 = df[df.method == 'Eclipse Timing Variations']
In [28]: df = DataFrame(np.random.randn(1000000,10))
In [29]: df
Out[29]:
<class 'pandas.core.frame.DataFrame'>
Int64Index: 1000000 entries, 0 to 999999
Data columns (total 10 columns):
0 1000000 non-null values
1 1000000 non-null values
2 1000000 non-null values
3 1000000 non-null values
4 1000000 non-null values
5 1000000 non-null values
6 1000000 non-null values
7 1000000 non-null values
8 1000000 non-null values
9 1000000 non-null values
dtypes: float64(10)
In [30]: frames = [ df.iloc[i*60:min((i+1)*60,len(df))] for i in xrange(int(len(df)/60.) + 1) ]
In [31]: %timeit [ df.iloc[i*60:min((i+1)*60,len(df))] for i in xrange(int(len(df)/60.) + 1) ]
1 loops, best of 3: 849 ms per loop
In [32]: len(frames)
Out[32]: 16667
Here's a groupby way (and you could do an arbitrary apply rather than sum)
In [9]: g = df.groupby(lambda x: x/60)
In [8]: g.sum()
Out[8]:
<class 'pandas.core.frame.DataFrame'>
Int64Index: 16667 entries, 0 to 16666
Data columns (total 10 columns):
0 16667 non-null values
1 16667 non-null values
2 16667 non-null values
3 16667 non-null values
4 16667 non-null values
5 16667 non-null values
6 16667 non-null values
7 16667 non-null values
8 16667 non-null values
9 16667 non-null values
dtypes: float64(10)
Sum is cythonized that's why this is so fast
In [10]: %timeit g.sum()
10 loops, best of 3: 27.5 ms per loop
In [11]: %timeit df.groupby(lambda x: x/60)
1 loops, best of 3: 231 ms per loop
The method based on list comprehension and groupby- Which stores all the split dataframe in list variable and can be accessed using the index.
Example
ans = [pd.DataFrame(y) for x, y in DF.groupby('column_name', as_index=False)]
ans[0]
ans[0].column_name
You can use the groupby command, if you already have some labels for your data.
out_list = [group[1] for group in in_series.groupby(label_series.values)]
Here's a detailed example:
Let's say we want to partition a pd series using some labels into a list of chunks
For example, in_series is:
2019-07-01 08:00:00 -0.10
2019-07-01 08:02:00 1.16
2019-07-01 08:04:00 0.69
2019-07-01 08:06:00 -0.81
2019-07-01 08:08:00 -0.64
Length: 5, dtype: float64
And its corresponding label_series is:
2019-07-01 08:00:00 1
2019-07-01 08:02:00 1
2019-07-01 08:04:00 2
2019-07-01 08:06:00 2
2019-07-01 08:08:00 2
Length: 5, dtype: float64
Run
out_list = [group[1] for group in in_series.groupby(label_series.values)]
which returns out_list a list of two pd.Series:
[2019-07-01 08:00:00 -0.10
2019-07-01 08:02:00 1.16
Length: 2, dtype: float64,
2019-07-01 08:04:00 0.69
2019-07-01 08:06:00 -0.81
2019-07-01 08:08:00 -0.64
Length: 3, dtype: float64]
Note that you can use some parameters from in_series itself to group the series, e.g., in_series.index.day
here's a small function which might help some (efficiency not perfect probably, but compact + more or less easy to understand):
def get_splited_df_dict(df: 'pd.DataFrame', split_column: 'str'):
"""
splits a pandas.DataFrame on split_column and returns it as a dict
"""
df_dict = {value: df[df[split_column] == value].drop(split_column, axis=1) for value in df[split_column].unique()}
return df_dict
it converts a DataFrame to multiple DataFrames, by selecting each unique value in the given column and putting all those entries into a separate DataFrame.
the .drop(split_column, axis=1) is just for removing the column which was used to split the DataFrame. the removal is not necessary, but can help a little to cut down on memory usage after the operation.
the result of get_splited_df_dict is a dict, meaning one can access each DataFrame like this:
splitted = get_splited_df_dict(some_df, some_column)
# accessing the DataFrame with 'some_column_value'
splitted[some_column_value]
The existing answers cover all good cases and explains fairly well how the groupby object is like a dictionary with keys and values that can be accessed via .groups. Yet more methods to do the same job as the existing answers are:
Create a list by unpacking the groupby object and casting it to a dictionary:
dict([*df.groupby('Name')]) # same as dict(list(df.groupby('Name')))
Create a tuple + dict (this is the same as #jezrael's answer):
dict((*df.groupby('Name'),))
If we only want the DataFrames, we could get the values of the dictionary (created above):
[*dict([*df.groupby('Name')]).values()]
I had similar problem. I had a time series of daily sales for 10 different stores and 50 different items. I needed to split the original dataframe in 500 dataframes (10stores*50stores) to apply Machine Learning models to each of them and I couldn't do it manually.
This is the head of the dataframe:
I have created two lists;
one for the names of dataframes
and one for the couple of array [item_number, store_number].
list=[]
for i in range(1,len(items)*len(stores)+1):
global list
list.append('df'+str(i))
list_couple_s_i =[]
for item in items:
for store in stores:
global list_couple_s_i
list_couple_s_i.append([item,store])
And once the two lists are ready you can loop on them to create the dataframes you want:
for name, it_st in zip(list,list_couple_s_i):
globals()[name] = df.where((df['item']==it_st[0]) &
(df['store']==(it_st[1])))
globals()[name].dropna(inplace=True)
In this way I have created 500 dataframes.
Hope this will be helpful!
Tried iterrows() very slow, read elsewhere zip would be better, but it is still very slow.
I tried to search through rows of a dataframe, generate some stats to fill in two new dataframe.
Any suggestion to speed-up the searching through rows of dataframe?
Code snippets:
for index,date,stocknum in zip(stockpicks.index.values,stockpicks.date.values,stockpicks.stocknum.values):
stock=readStockPrice(stocknum)
if stock.empty:
return print("error - empty frame")
stock=stock.ix[trading_days]
stockprice=stock.Close.values
p0_date=trading_days.get_loc(date)
p0=stockprice[p0_date]
stock_pct_change={('d'+str(d)):stockprice[p0_date+d]/p0*100.0 if (p0_date+d)< len(trading_days) else np.nan for d in days }
b0=hsi[p0_date]
benchmark_pct_change={('d'+str(d)):hsi[p0_date+d]/b0*100.0 if (p0_date+d)< len(trading_days) else np.nan for d in days }
for d in days:
stock_analysis.loc[index,'d'+str(d)]=stock_pct_change['d'+str(d)]
benchmark_analysis.loc[index,'d'+str(d)]=benchmark_pct_change['d'+str(d)]
Your problem appears can be vectorized completely. Iteration and indexing like you are doing is the slowest possible way.
In [6]: df = DataFrame(np.random.randint(-5,5,size=20).reshape(5,4),columns=list('abcd'),index=date_range('20130101',periods=5))+50.0
In [7]: df.pct_change()
Out[7]:
a b c d
2013-01-01 NaN NaN NaN NaN
2013-01-02 0.108696 0.108696 0.102041 0.086957
2013-01-03 -0.058824 -0.039216 -0.074074 -0.060000
2013-01-04 0.104167 0.081633 -0.020000 0.000000
2013-01-05 -0.075472 -0.113208 0.061224 -0.021277
[5 rows x 4 columns]