Save data frame from inside for loop - python
I have a function that takes in a dataframe and returns a (reduced) dataframe, e.g. like this:
def transforming_data(dataframe, col_1, col_2, normalized = True):
''' takes in dataframe, groups col_1 according to col_2 and returns dataframe
'''
df = dataframe[col_1].groupby(dataframe[col_2]).value_counts(normalize = normalized).unstack(fill_value = 0)
return dataframe
For the following code, this gives me:
import pandas as pd
import numpy as np
np.random.seed(12)
def transforming_data(df, col_1, col_2, normalized = True):
''' takes in df, groups col_1 according to col_2 and returns df '''
df = dataframe[col_1].groupby(dataframe[col_2]).value_counts(normalize = normalized).unstack(fill_value = 0)
return df
numrows = 1000
dataframe = pd.DataFrame({'Numerical': np.random.randn(numrows),
'Category': np.random.choice(['Panda', 'Elephant', 'Anaconda'], numrows),
'Response 1': np.random.choice(['Yes', 'Maybe', 'No', 'Don\'t know'], numrows),
'Response 2': np.random.choice(['Very Much', 'Much', 'A bit', 'Not at all'], numrows)})
test = transforming_data(dataframe, 'Response 1', 'Category')
print(test)
# Output
# Response 1 Don't know Maybe No Yes
# Category
# Anaconda 0.275229 0.232416 0.217125 0.275229
# Elephant 0.220588 0.270588 0.255882 0.252941
# Panda 0.258258 0.222222 0.273273 0.246246
So far, so good.
Now I want to use the function transforming_data inside a for loop for every column in dataframe (as I have lots of columns, not just two) and save the resulting dataframe to a new dataframe, e.g. test_response_1 and test_response_2 for this example.
Can someone point me in the right direction - i.e. how to implement the loop correctly?
So far, I am using something like this - but cannot figure out how to save the data frame
for column in dataframe.columns.tolist():
temp_df = transforming_data(dataframe, column, 'Category')
# here, I need to save tmp_df outside of the loop but don't know how to
Thanks a lot for pointers and help. (Note: the most similar question I found does not talk about actually saving the data frame, so it doesn't help me with this.
If you want to save (in memory) all of the temp_df's from your loop, you can append them to a list that you can then index afterwards:
temp_dfs = []
for column in dataframe.columns.tolist(): #you don't actually need the tolist() method here
temp_df = transforming_data(dataframe, column, 'Category')
temp_dfs.append(temp_df)
If you rather be able to access these temp_df's by the column name that was used to transform them, then you could assign each to a dictionary, using the column as the key:
temp_dfs = {}
for column in dataframe.columns.tolist():
temp_df = transforming_data(dataframe, column, 'Category')
temp_dfs[column] = temp_df
If by "save" you meant "write to disk", then you can use one of the many to_<file_format>() methods that pandas provides:
temp_dfs = {}
for column in dataframe.columns.tolist():
temp_df = transforming_data(dataframe, column, 'Category')
temp_df.to_csv('temp_df{}.csv'.format(column))
Here's the to_csv() docs.
The most simple solution would be to save the result dataframes into a list. Assuming that all columns that you want to loop over have the text Response in their column name:
result_dframes = []
for col_name in dataframe.filter(like='Response').columns:
result_dframe = transforming_data(dataframe, col_name, 'Category')
result_dframes.append(result_dframe)
Alternatively you can also obtain the exact same result with a list comprehension instead of a for-loop:
result_dframes = [
transforming_data(dataframe, col_name, 'Category')
for col_name in dataframe.filter(like='Response')
]
Related
Add values from a nested JSON to a pandas dataframe
I have the following JSON object: {"code":"Ok","matchings":[{"confidence":0.025755,"geometry":"qnp{bBww{kH??~D_I}E_J{EaJ{E{I{AsCoJgQfKuTjJwNtF}HdBuBnAgBpFsF~EeEzAsAt#i#lA}#x#q#lEmCjDuBdDoAvFmAfYmEtAUrJyDj#_#h#m#`#u#T}#J{#B_A?gAGmAM}#Su#]u#wN{QwI{KcA}Aa#gASiAWsBOwCGmDCoJ??cEH?{FA{HgIXuG`#eHrAsLdDkI|CkIfDq#VoDlB_GzDaE`D_A|#kA`AeAx#sI~G}DlDk#j#mClCiOrQwGvJiGxJoFdK_HjP{Pne#aLt\\sK~]oKb_#sG~TeJ`_#q#fD{#dEoBlMwBxQaAbI{Dh\\wKrfAiRbvBy#`KaLjwAyHj_AANM~AUxC}#tKi#bHe#jGfBj#t#V|#\\TFjAXz#HhASxAy#vCcBjX~GvG`BlEjAv\\xJfBf#dThG~Ad#nFrBnCbBdCvBzB`DbCfEr{#b~A","legs":[{"annotation":{"nodes":[330029575,5896466632,330029575,5896466588,5896466587,5896466586,5896466637,330029340,330029339,330029338,1497356855,1880770263,46388213,1880770262,1880770257,2021835257,3306177380,46387099,2021835255,6909770873,46385948,6909770874,46384887,46382454]},"steps":[],"distance":332.2,"duration":93.1,"summary":"","weight":93.1},{"annotation":{"nodes":[46384887,46382454,5888264001,6909802199,3296872014,6909802198,5888264003,6909802197,3296872012,6909802194,6909802195,6909802193,6909802196,3296872013,3296872015]},"steps":[],"distance":88.1,"duration":13.5,"summary":"","weight":13.5},{"annotation":{"nodes":[3296872013,3296872015,6909802186,6909802187,6909770884,3296872017,6909802185,4904066416,3296872018,1614187163]},"steps":[],"distance":62.3,"duration":12.4,"summary":"","weight":12.4},{"annotation":{"nodes":[3296872018,1614187163,2054127599,1614187129,5896479942,6909802219,46384372,1027299576,6909802220,46389815]},"steps":[],"distance":144,"duration":25.2,"summary":"","weight":25.2},{"annotation":{"nodes":[6909802220,46389815,6296436095,6296436094,298079716,6296436096,46391324,1083528076,6909802221,6909802222,46393158]},"steps":[],"distance":90.6,"duration":10.1,"summary":"","weight":10.1},{"annotation":{"nodes":[6909802222,46393158,46393795,6909802223,1027299602,6909802224,46396846,46398397,2054127645,46399502,46400708,1027299589,6712474212,6903665704,46402805,46403163,4374153462]},"steps":[],"distance":422.9,"duration":40.1,"summary":"","weight":40.1},{"annotation":{"nodes":[46403163,4374153462,46404084,1027299603,364146312,2262500170]},"steps":[],"distance":273.6,"duration":24.7,"summary":"","weight":24.7},{"annotation":{"nodes":[364146312,2262500170,5289718695]},"steps":[],"distance":170.9,"duration":15.3,"summary":"","weight":15.3},{"annotation":{"nodes":[2262500170,5289718695,2054127657,1693195716,46408565,6913837768,1693195721,2262500247,1693195714,2262500104,1693195717]},"steps":[],"distance":56.9,"duration":14.2,"summary":"","weight":14.2},{"annotation":{"nodes":[46397705,46401323,46405521]},"steps":[],"distance":86.6,"duration":12.6,"summary":"","weight":12.6},{"annotation":{"nodes":[46401323,46405521,46410773]},"steps":[],"distance":156.5,"duration":22.5,"summary":"","weight":22.5},{"annotation":{"nodes":[46405521,46410773,452003319,452003320]},"steps":[],"distance":95.4,"duration":13.8,"summary":"","weight":13.8},{"annotation":{"nodes":[452003319,452003320,46411428,46414457,46419384,46421801]},"steps":[],"distance":226.4,"duration":32.6,"summary":"","weight":32.6},{"annotation":{"nodes":[46419384,46421801,46421802,46421735]},"steps":[],"distance":69.2,"duration":10,"summary":"","weight":10},{"annotation":{"nodes":[46421802,46421735,46421416]},"steps":[],"distance":34.1,"duration":4.9,"summary":"","weight":4.9},{"annotation":{"nodes":[46421735,46421416,46420466]},"steps":[],"distance":2.7,"duration":0.3,"summary":"","weight":0.3},{"annotation":{"nodes":[46421416,46420466]},"steps":[],"distance":31.4,"duration":4.6,"summary":"","weight":4.6},{"annotation":{"nodes":[46421416,46420466,452003307,452003308,46421260,46422467,5761752102,46423905]},"steps":[],"distance":135.5,"duration":25,"summary":"","weight":25},{"annotation":{"nodes":[5761752102,46423905,46424346,5777055555,5713213408,46425605,5777055050,5777346784,5777055556,5713221227,46426685,46427741,3175895442,3183752428,5826014405,46428227]},"steps":[],"distance":106.5,"duration":14.9,"summary":"","weight":14.9},{"annotation":{"nodes":[5826014405,46428227,3175895443,5826014406,3175895444,5826014368,5826014369,5826014374,46429570,5826014373,5826014375,5826014372,5826014358,5826014371,5826014370,5826014376]},"steps":[],"distance":172.7,"duration":15.7,"summary":"","weight":15.7},{"annotation":{"nodes":[2054127660,2054127638,2054127605,6296435009,2054127599,6909770882,3296872018,4904066416,6909802185,3296872017,6909770884,6909802187,6909802186,3296872015,3296872013,6909802196,6909802193,6909802195,6909802194,3296872012,6909802197,5888264003,6909802198,3296872014,6909802199,5888264001,46382454,46384887,6909770874,46385948,6909770873,2021835255,46387099,3306177380,2021835257]},"steps":[],"distance":317.7,"duration":46.1,"summary":"","weight":46.1},{"annotation":{"nodes":[3306177380,2021835257,1880770257,1880770262,46388213,1880770263,1497356855,330029338,330029339,330029340,5896466637]},"steps":[],"distance":150.4,"duration":29.4,"summary":"","weight":29.4}],"distance":80317.8,"duration":10983.5,"weight_name":"duration","weight":10983.5}],"tracepoints":[{"alternatives_count":0,"waypoint_index":0,"matchings_index":0,"location":[4.929932,52.372217],"name":"Willem Theunisse Blokstraat","distance":10.791613,"hint":"CAkHgHAJBwAlAAAAAAAAAAAAAAAAAAAALCd0QQAAAAAAAAAAAAAAACUAAAAAAAAAAAAAAAAAAAABAAAAjDlLAPkiHwP3OEsAGiMfAwAArxMz7Ejh"},null,{"alternatives_count":0,"waypoint_index":1,"matchings_index":0,"location":[4.932506,52.3709],"name":"Frans de Wollantstraat","distance":11.915926,"hint":"pwUBAPYEAYAHAAAARwAAAAAAAAAAAAAA3_qaQE0JPUIAAAAAAAAAAAcAAABHAAAAAAAAAAAAAAABAAAAmkNLANQdHwPtQksAxB0fAwAA_xUz7Ejh"},{"alternatives_count":0,"waypoint_index":472,"matchings_index":0,"location":[4.932745,52.373288],"name":"Piet Heinkade","distance":0.98867,"hint":"gwUBgMgFAQAFAAAADQAAABoBAABYAAAAQMS3QHTNW0HsWZ1DmZ2WQgUAAAANAAAAGgEAAFgAAAABAAAAiURLACgnHwN9REsAIycfAwoADwkz7Ejh"},null,null,{"alternatives_count":1,"waypoint_index":473,"matchings_index":0,"location":[4.934022,52.371637],"name":"Piet Heinkade","distance":2.713742,"hint":"NA8HADsPB4ACAAAADwAAADoAAAA-AAAAjU82QIAqg0FUpSdCLoWJQgIAAAAPAAAAOgAAAD4AAAABAAAAhklLALUgHwNfSUsAsCAfAwQAvxUz7Ejh"},null,null,{"alternatives_count":1,"waypoint_index":474,"matchings_index":0,"location":[4.93213,52.371794],"name":"Frans de Wollantstraat","distance":10.337677,"hint":"AgUBgAcFAQABAAAABAAAAAwAAAAAAAAA1paeP-KrBUAomAdBAAAAAAEAAAAEAAAADAAAAAAAAAABAAAAIkJLAFIhHwOrQksAeiEfAwIA7xQz7Ejh"},{"alternatives_count":1,"waypoint_index":475,"matchings_index":0,"location":[4.93074,52.372528],"name":"Isaac Titsinghkade","distance":0.65222,"hint":"AwkHgAYJBwA5AAAACwAAAAAAAACMAAAA_Fe_QWP_k0AAAAAA33FqQjkAAAALAAAAAAAAAIwAAAABAAAAtDxLADAkHwOtPEsANCQfAwAADw4z7Ejh"},null,null]} I want to add all values that belong to the key nodes to one column in a pandas dataframe When I run: for i in output["matchings"][0]['legs']: result = i['annotation']['nodes'] df = pd.DataFrame(result, columns=['node']) df only a fraction gets added to the dataframe. What am I doing wrong?
At the end of your for loop, 'df' keeps the last 'node' key of your json. You have to append all 'nodes' keys in a single dataframe instead. Extending your code: df = pd.DataFrame({'node':{}}) for i in output["matchings"][0]['legs']: result = i['annotation']['nodes'] df_temp = pd.DataFrame(result, columns=['node']) df = df.append(df_temp, ignore_index=True)
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