What is the difference between an h5 and hdf file? Should I use one over the other? I tried doing a timeit with the following two codes and it took about 3 minutes and 29 seconds per loop and with a 240mb file. I had an error eventually on the second code but the file size was above 300mb on disk.
hdf = pd.HDFStore('combined.h5')
hdf.put('table', df, format='table', complib='blosc', complevel=5, data_columns=True)
hdf.close()
df.to_hdf('combined.hdf', 'table', format='table', mode='w', complib='blosc', complevel=5)
Also, I had an error message which said:
your performance may suffer as PyTables will pickle object types that it cannot
map directly to c-types
This is due to string columns which are objects because of blank values. If I do .astype(str), all of the blanks are replaced with nan (a string which even appears in output files). Do I worry about the error message and fill in the blanks and replace them again with np.nan later, or just ignore it?
Here is the df.info() to show that there are some columns with nulls. I can't remove these rows but I could temporarily fill them with something if required.
<class 'pandas.core.frame.DataFrame'>
Int64Index: 1387276 entries, 0 to 657406
Data columns (total 12 columns):
date 1387276 non-null datetime64[ns]
start_time 1387276 non-null datetime64[ns]
end_time 313190 non-null datetime64[ns]
cola 1387276 non-null object
colb 1387276 non-null object
colc 1387276 non-null object
cold 476816 non-null object
cole 1228781 non-null object
colx 1185679 non-null object
coly 313190 non-null object
colz 1387276 non-null int64
colzz 1387276 non-null int64
dtypes: datetime64[ns](3), int64(2), object(7)
memory usage: 137.6+ MB
Related
I would display all information of my data frame which contains more than 100 columns with .info() from pandas but it won't :
data_train.info()
<class 'pandas.core.frame.DataFrame'>
RangeIndex: 85529 entries, 0 to 85528
Columns: 110 entries, ID to TARGET
dtypes: float64(40), int64(19), object(51)
memory usage: 71.8+ MB
I would like it displays like this :
data_train.info()
<class 'pandas.core.frame.DataFrame'>
RangeIndex: 10886 entries, 0 to 10885
Data columns (total 12 columns):
datetime 10886 non-null object
season 10886 non-null int64
holiday 10886 non-null int64
workingday 10886 non-null int64
weather 10886 non-null int64
temp 10886 non-null float64
atemp 10886 non-null float64
humidity 10886 non-null int64
windspeed 10886 non-null float64
casual 10886 non-null int64
registered 10886 non-null int64
count 10886 non-null int64
dtypes: float64(3), int64(8), object(1)
memory usage: 1020.6+ KB
But the problem seems to be the high number of columns from my previous data frame. I would like to show all values including non null values (NaN).
You can pass optional arguments verbose=True and show_counts=True (null_counts=True deprecated since pandas 1.2.0) to the .info() method to output information for all of the columns
pandas >=1.2.0: data_train.info(verbose=True, show_counts=True)
pandas <1.2.0: data_train.info(verbose=True, null_counts=True)
I am seeing some strange behavior when trying to use pd.concat. I have a list of dataframes, with variables of one type (in this instance categorical) which get changed to objects when I concatenate them. The df is massive and this makes it even larger - too large to deal with.
Here is some sample code:
As context, I have scraped a website for a bunch of CSV files. I am reading, cleaning and setting the dtypes of all of them before appending them to a list. I then concatenate all the dfs in that list (but the dtypes of some variables get changed).
#Import modules
import glob
import pandas as pd
#Code to identify and download all the csvs
###
#code not included - seemed excessive
###
#Identify all the downloaded csvs
modis_csv_files = glob.glob('/path/to/files/**/*.csv', recursive = True)
#Examine the dtypes of one of these files
pd.read_csv(modis_csv_files[0]).info()
<class 'pandas.core.frame.DataFrame'>
RangeIndex: 6 entries, 0 to 5
Data columns (total 15 columns):
# Column Non-Null Count Dtype
--- ------ -------------- -----
0 latitude 6 non-null float64
1 longitude 6 non-null float64
2 brightness 6 non-null float64
3 scan 6 non-null float64
4 track 6 non-null float64
5 acq_date 6 non-null object
6 acq_time 6 non-null int64
7 satellite 6 non-null object
8 instrument 6 non-null object
9 confidence 6 non-null int64
10 version 6 non-null float64
11 bright_t31 6 non-null float64
12 frp 6 non-null float64
13 daynight 6 non-null object
14 type 6 non-null int64
dtypes: float64(8), int64(3), object(4)
memory usage: 848.0+ bytes
We can see a number of object dtypes in there that will make the final df larger. So now I try read all the files, and set the dtype as i go.
#Read the CSVs, clean them and append them to a list
outputs = [] #Create the list
counter = 1 #Start a counter as i am importing around 4000 files
for i in modis_csv_files: #Iterate over the files importing and cleaning
print('Reading csv no. {} of {}'.format(counter, len(modis_csv_files))) #Produce a print statement describing progress
output = pd.read_csv(i) #Read the csv
output[['daynight', 'instrument', 'satellite']] = output[['daynight', 'instrument', 'satellite']].apply(lambda x: x.astype('category')) #Set the dtype for all the object variables that can be categories
output['acq_date'] = output['acq_date'].astype('datetime64[ns]') #Set the date variable
outputs.append(output) #Append to the list
counter += 1 #Increment the counter
#Conetenate all the files
final_modis = pd.concat(outputs)
#Look at the dtypes
final_modis.info()
<class 'pandas.core.frame.DataFrame'>
Int64Index: 85604183 entries, 0 to 24350
Data columns (total 15 columns):
# Column Dtype
--- ------ -----
0 latitude float64
1 longitude float64
2 brightness float64
3 scan float64
4 track float64
5 acq_date datetime64[ns]
6 acq_time int64
7 satellite object
8 instrument category
9 confidence int64
10 version float64
11 bright_t31 float64
12 frp float64
13 daynight object
14 type int64
dtypes: category(1), datetime64[ns](1), float64(8), int64(3), object(2)
memory usage: 9.6+ GB
Notice that satellite and daynight still show as object (though notably instrument stays as category). So I check if there is a problem with my cleaning code.
outputs[0].info()
<class 'pandas.core.frame.DataFrame'>
RangeIndex: 6 entries, 0 to 5
Data columns (total 15 columns):
# Column Non-Null Count Dtype
--- ------ -------------- -----
0 latitude 6 non-null float64
1 longitude 6 non-null float64
2 brightness 6 non-null float64
3 scan 6 non-null float64
4 track 6 non-null float64
5 acq_date 6 non-null datetime64[ns]
6 acq_time 6 non-null int64
7 satellite 6 non-null category
8 instrument 6 non-null category
9 confidence 6 non-null int64
10 version 6 non-null float64
11 bright_t31 6 non-null float64
12 frp 6 non-null float64
13 daynight 6 non-null category
14 type 6 non-null int64
dtypes: category(3), datetime64[ns](1), float64(8), int64(3)
memory usage: 986.0 bytes
Looks like everything changed. Perhaps one of the 4000 dfs contained something that meant they could not be changed to categorical, which caused the whole variable to shift back toobject when concatenated. Try checking each df in the list to see if either satellite or daynight is not category:
error_output = [] #create an empty list
for i in range(len(outputs)): #iterate over the list checking if dtype['variable'].name is categorical
if outputs[i].dtypes['satellite'].name != 'category' or outputs[i].dtypes['daynight'].name != 'category':
error_output.append(outputs[i]) #if not, append
#Check what is in the list
len(error_output)
0
So there are no dataframes in the list for which either of these variables is not categorical, but when I concatenate them the resulting variables are objects. Notably this outcome does not apply to all categorical variables, as instrument doesn't get changed back. What is going on?
Note: I can't change the dtype after pd.concat, because I run out of memory (I know there are some other solutions to this, but I am still intrigued by the behavior of pd.concat).
FWIW i am scraping data from the modis sattelite: https://firms.modaps.eosdis.nasa.gov/download/ (yearly summary by country). I can share all the scraping code as well if that would be helpful (seemed excessive for now however).
I have a dataframe (see link for image) and I've listed the info on the data frame. I use the pivot_table function to sum the total number of births for each year. The issue is that when I try to plot the dataframe, the y-axis values range from 0 to 2.0 instead of the minimum and maximum values from the M and F columns.
To verify that it's not my environment, I created a simple dataframe, with just a few values and plot the line graph for that dataframe and it works as expected. Does anyone know why this is happening? Attempting to set the values using ylim or yticks is not working. Ultimately, I will have to try other graphing utilities like matplotlib, but I'm curious as to why it's not working for such a simple dataframe and dataset.
Visit my github page for a working example <git#github.com:stevencorrea-chicago/stackoverflow_question.git>
<class 'pandas.core.frame.DataFrame'>
RangeIndex: 1690784 entries, 0 to 1690783
Data columns (total 4 columns):
# Column Non-Null Count Dtype
--- ------ -------------- -----
0 name 1690784 non-null object
1 sex 1690784 non-null object
2 births 1690784 non-null int64
3 year 1690784 non-null Int64
dtypes: Int64(1), int64(1), object(2)
memory usage: 53.2+ MB
new_df = df.pivot_table(values='births', index='year', columns='sex', aggfunc=sum)
new_df.info()
<class 'pandas.core.frame.DataFrame'>
Index: 131 entries, 1880 to 2010
Data columns (total 2 columns):
# Column Non-Null Count Dtype
--- ------ -------------- -----
0 F 131 non-null int64
1 M 131 non-null int64
dtypes: int64(2)
memory usage: 3.1+ KB
In an attempt to make my pandas code faster I installed modin and tried to use it. A merge of two data frames that had previously worked gave me the following error:
ValueError: can not merge DataFrame with instance of type <class 'pandas.core.frame.DataFrame'>
Here is the info of both data frames:
printing event_df.info
<class 'pandas.core.frame.DataFrame'>
RangeIndex: 1980101 entries, 0 to 1980100
Data columns (total 5 columns):
other_id object
id object
category object
description object
date datetime64[ns]
dtypes: datetime64[ns](1), object(4)
memory usage: 75.5+ MB
printing other_df info
<class 'pandas.core.frame.DataFrame'>
RangeIndex: 752438 entries, 0 to 752437
Data columns (total 4 columns):
id 752438 non-null object
other_id 752438 non-null object
Value 752438 non-null object
Unit 752438 non-null object
dtypes: object(4)
memory usage: 23.0+ MB
Here are some rows from event_df:
other_id id category description date
08E5A97350FC8B00092F 1 some_string some_string 2019-04-09
17B71019E148415D 4 some_string some_string 2019-11-08
17B71019E148415D360 7 some_string some_string 2019-11-08
and here are 3 rows from other_df:
id other_id Value Unit
a01 BE4F15A3AE8A508ACB45F0FC8CDC173D1628D283 3 some_string
a02 BE4F15A3AE8A508ACB45F0FC8CDC173D1628D283 3 some_string
a03 BE4F15A3AE8A508ACB45F0FC8CDC173D1628D283 3 some_string
I tried installing the version cited in this question Join two modin.pandas.DataFrame(s), but it didn't help.
Here's the line of code throwing the error:
joint_dataframe2 = pd.merge(event_df,other_df, on = ["id","other_id"])
It seems there is some problem with modin's merge functionality. Is there any workaround such as using pandas for the merge and using modin for a groupby.transform()? I tried overwriting the pandas import after the merge with import modin.pandas, but got an error saying pandas was referenced before assignment. Has anyone come across this problem and if so, is there a solution?
Your error reads like you were merging an instance of modin.pandas.dataframe.DataFrame with an instance of pandas.core.frame.DataFrame, which is not allowed.
If that's indeed the case, you could turn the pandas Dataframe into a modin Dataframe first, then you should be able to merge them, I believe.
I am using two different data sets (linked below) which observe geographic location. I am trying to drop all observations where State is a territory or other non-State (excluding DC). The 'State' variable/column is a non-null object in both dataframes. I include .info() on them to show data types and number of observations.
Earlier in my code I use .isin() for this purpose for a different dataframe and it works:
zipcodes = pd.read_csv('zipcode.csv')
zipcodes.info()
<class 'pandas.core.frame.DataFrame'>
RangeIndex: 42522 entries, 0 to 42521
Data columns (total 12 columns):
Zipcode 42522 non-null int64
ZipCodeType 42522 non-null object
City 42522 non-null object
State 42522 non-null object
LocationType 42522 non-null object
Lat 41874 non-null float64
Long 41874 non-null float64
Location 42521 non-null object
Decommisioned 42522 non-null bool
TaxReturnsFiled 28879 non-null float64
EstimatedPopulation 28879 non-null float64
TotalWages 28844 non-null float64
dtypes: bool(1), float64(5), int64(1), object(5)
memory usage: 2.8+ MB
drops =['PR','AP','AA','VI','GU','FM','MP','MH','PW','AS','AE']
zipcodes = zipcodes[~zipcodes['State'].isin(drops)]
zipcodes.info()
<class 'pandas.core.frame.DataFrame'>
Int64Index: 41656 entries, 12 to 42521
Data columns (total 12 columns):
Zipcode 41656 non-null int64
ZipCodeType 41656 non-null object
City 41656 non-null object
State 41656 non-null object
LocationType 41656 non-null object
Lat 41656 non-null float64
Long 41656 non-null float64
Location 41656 non-null object
Decommisioned 41656 non-null bool
TaxReturnsFiled 28879 non-null float64
EstimatedPopulation 28879 non-null float64
TotalWages 28844 non-null float64
dtypes: bool(1), float64(5), int64(1), object(5)
memory usage: 3.1+ MB
The observations for which State is an object in the list drops have now been dropped.
However, when I try to do the same with another data set, the same .isin() code does not drop the observations for which State is in the list drops (I have to create the State variable by splitting an included variable and then adding it as a column, which I suspect may be causing my issue, but the resulting variable/column is still a non-null object):
cty_nbrs = pd.read_excel('county_adjacency.xlsx')
cty_nbrs.fillna(method='ffill', inplace=True)
cty_nbrs.join(pd.DataFrame({'State':cty_nbrs.County.str.split(",").str.get(1)}))
cty_nbrs.info()
<class 'pandas.core.frame.DataFrame'>
RangeIndex: 22200 entries, 0 to 22199
Data columns (total 5 columns):
County 22200 non-null object
ctyfips 22200 non-null int32
Neighbours 22200 non-null object
nbrfips 22200 non-null int64
State 22200 non-null object
dtypes: int32(1), int64(1), object(3)
memory usage: 520.4+ KB
cty_nbrs= cty_nbrs[~cty_nbrs['State'].isin(drops)]
cty_nbrs.info()
<class 'pandas.core.frame.DataFrame'>
RangeIndex: 22200 entries, 0 to 22199
Data columns (total 5 columns):
County 22200 non-null object
ctyfips 22200 non-null int32
Neighbours 22200 non-null object
nbrfips 22200 non-null int64
State 22200 non-null object
dtypes: int32(1), int64(1), object(3)
memory usage: 520.4+ KB
The offending observations have not been dropped here. In case this is not evident:
cty_nbrs['State'].value_counts().tail()
AS 7
MP 6
DC 6
VI 5
GU 1
Name: State, dtype: int64
Those realizations of State are all elements of the list drops which were earlier dropped from zipcodes using the same code. What am I missing here?
zipcode.csv
county_adjacency.csv