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Want a pandas Series of Trips Completed to count(Request) ratio for each hour as index for the given dataframe


Want a pandas Series of Trips Completed to count(Request) ratio for each hour as index for the given dataframe

By : user3042618
Date : November 28 2020, 12:01 PM
it should still fix some issue The link to the dataset: 'https://drive.google.com/file/d/19P0Trh_vJ2UuC9qZi7xLL_ZFTeKLTvWR/view?usp=sharing' , Data manipulation
code :
agg = df.pivot_table(index=["Req_hour", "Status", "Pickup point"], values=["Request id"], aggfunc="count").rename(columns={"Request id": "Count"}).reset_index()
hr_totals = df[["Req_hour", "Request id"]].groupby(["Req_hour"]).count().rename(columns={"Request id": "Total"}).reset_index()
merged = agg.merge(hr_totals, on="Req_hour")
merged['Ratio'] = merged['Count'] / merged['Total']
Req_hour    Status  Pickup point    Count   Total   Ratio
0   0   Cancelled   City    3   99  0.030303
1   0   No Cars Available   Airport 30  99  0.303030
2   0   No Cars Available   City    26  99  0.262626
3   0   Trip Completed  Airport 23  99  0.232323
4   0   Trip Completed  City    17  99  0.171717
5   1   Cancelled   City    4   85  0.047059
6   1   No Cars Available   Airport 29  85  0.341176
7   1   No Cars Available   City    27  85  0.317647
8   1   Trip Completed  Airport 13  85  0.152941
9   1   Trip Completed  City    12  85  0.141176
sns.catplot(x="Req_hour", y="Ratio", hue="Status", row="Pickup point", data=merged, kind="bar", size=6, aspect=2)


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pandas lookup daily series value for half-hour dataframe index

pandas lookup daily series value for half-hour dataframe index


By : JoAnne Wickwire Lath
Date : March 29 2020, 07:55 AM
Does that help I have a pandas dataframe with a half-hour timeseries index and a series of daily data that I need to match based on date for an equation. The following code works using .get() in a loop but is slow and seems rather "unpythonic." , you can do this pandathonically:
first, get the date-only field:
code :
df_t['Date'] = pd.to_datetime(df_t.index.date)
df_t = df_t.reset_index().set_index('Date')
df_t['E'] = ts_d
df_t = df_t.reset_index().set_index('index')
df_t.ix[pd.to_datetime('20130102')]
what is the best way to merge pandas.Dataframe with pandas.Series based on df.columns and Series.index names?

what is the best way to merge pandas.Dataframe with pandas.Series based on df.columns and Series.index names?


By : Gambler
Date : March 29 2020, 07:55 AM
help you fix your problem Suppose that you create these series as outputs output_rms_1, output_rms_2, etc., than the series can be combined in one dataframe
code :
import pandas as pd
dfRms = pd.DataFrame([output_rms_1, output_rms_2, output_rms_3])
dfRms = dfRms.append(output_rms_10, ignore_index=True)
result = pd.merge(wfm, dfRms, on=['CFN', 'OPN'], how='left')
how to count start hour of day, end hour of day, average hours per day in pandas dataframe?

how to count start hour of day, end hour of day, average hours per day in pandas dataframe?


By : user2894740
Date : March 29 2020, 07:55 AM
I hope this helps . I have a dataframe: , IIUC you can try this:
code :
df['timestamps'] = pd.to_datetime(df['timestamps'])
df['hour'] = df['timestamps'].dt.hour
df.groupby(df['timestamps'].dt.day).agg({'hour': ['min', 'max', 'mean']}) \
                                   .stack(level=0).droplevel(1)


        min max mean
timestamps          
18      20  20  20.000000
19      12  22  14.400000
20      10  12  11.166667
Pandas - Add values from series to dataframe column based on index of series matching some value in dataframe

Pandas - Add values from series to dataframe column based on index of series matching some value in dataframe


By : user3531465
Date : March 29 2020, 07:55 AM
To fix this issue Data , I would use the second solution you propose or better this:
code :
df['cost']=(df['mark_up_id'].map(pb['mark_up']) + df['cost']).fillna(df['cost'])
df.assign( Cost=(df['mark_up_id'].map(pb['mark_up']) + df['cost']).fillna(df['cost']) )
%%timeit
df['cost']=(df['mark_up_id'].map(pb['mark_up']) + df['cost']).fillna(df['cost'])
#945 µs ± 46 µs per loop (mean ± std. dev. of 7 runs, 1000 loops each)
%%timeit
test = df.join(pb, on='mark_up_id', how='left')
test['cost'].update(test['cost'] + test['mark_up'])
test.drop('mark_up',axis=1,inplace=True)
#3.59 ms ± 137 µs per loop (mean ± std. dev. of 7 runs, 100 loops each)
%%timeit
df['cost'].update(df['mark_up_id'].map(pb['mark_up']) + df['cost'])
#985 µs ± 32.8 µs per loop (mean ± std. dev. of 7 runs, 1000 loops each)
Python pandas: merge dataframe to series on series's index and dataframe's column

Python pandas: merge dataframe to series on series's index and dataframe's column


By : ColoradoJake1
Date : March 29 2020, 07:55 AM
help you fix your problem You got to add a few things:
First, your counts_df has no column name, adding the name will get you a Dataframe with a column name
code :
counts_df=pd.DataFrame({'Topic_Num':counts.index, 'value':counts.values})
merge = counts_df.merge(topicKeywordsDf, left_index=True, right_on="Topic_Num").drop_duplicates()
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