Dataframe Set_Index Not Setting

Dataframe set_index not setting

You need to either specify inplace=True, or assign the result to a variable. Try:

df.set_index('Timestamp', inplace=True, drop=True)

Basically, there are two things that you might want to do when you set the index. One is new_df = old_df.set_index('Timestamp', inplace=False). I.e. You want a new DataFrame that has the new index, but still want a copy of the original DataFrame. The other is df.set_index('Timestamp', inplace=True). Which is for when you want to modify the existing object.

df.set_index() Not Working as What I Expected

You need assign back:

city_prop = city_prop.set_index('index')

Or:

city_prop.set_index('index', inplace = True)

---

EDIT:

df = pd.read_csv('CityProperEskwenilaExtraIndicators.csv', 
                 skiprows=1,
                 header=None, 
                 sep=';',
                 index_col=[0,1]).T

---

print (df.head())
0 Barangay    Longitude     Latitude        Poverty rate Terrain type  \
1        #    See annex    See annex Per 100 inhabitants    See annex   
2        1  27,67231183   66,3112793                  18    Difficult   
3        2  65,15620167  53,32027629                  54    Difficult   
4        3  34,94438385   89,7970517                  63    Difficult   
5        4  10,97542641  84,26323733                  42       Normal   
6        5  26,05436012  61,30689679                  70    Difficult   

0 Roads needing repair  Access to WASH Access to clean water  \
1   kilometers of road % of population       % of population   
2          55,40469584            50,2                  71,2   
3          14,08228761            51,8                  88,9   
4          33,20044684              77                  97,4   
5          1,695918463            74,7                  52,1   
6          85,08259271            70,1                  99,3   

0 Violent incidents     Homicides  
1     rate per 100K rate per 100K  
2              7,72   6,833797715  
3               8,3   5,513650409  
4              3,72   2,931838433  
5              6,26   5,883509349  
6              6,55   5,348430398  

---

#replace ,
df = df.replace(',','.', regex=True)
#remove second level
df.columns = df.columns.droplevel(1)
#convert columns to numeric
excluded = ['Terrain type','Poverty rate']
cols = df.columns.difference(excluded)
#to floats
df[cols] = df[cols].astype(float)
#to integer
df['Poverty rate'] = df['Poverty rate'].astype(int)
print (df.head())
0  Barangay  Longitude   Latitude  Poverty rate Terrain type  \
2       1.0  27.672312  66.311279            18    Difficult   
3       2.0  65.156202  53.320276            54    Difficult   
4       3.0  34.944384  89.797052            63    Difficult   
5       4.0  10.975426  84.263237            42       Normal   
6       5.0  26.054360  61.306897            70    Difficult   

0  Roads needing repair  Access to WASH  Access to clean water  \
2             55.404696            50.2                   71.2   
3             14.082288            51.8                   88.9   
4             33.200447            77.0                   97.4   
5              1.695918            74.7                   52.1   
6             85.082593            70.1                   99.3   

0  Violent incidents  Homicides  
2               7.72   6.833798  
3               8.30   5.513650  
4               3.72   2.931838  
5               6.26   5.883509  
6               6.55   5.348430

---

print (df.dtypes)
0
Barangay                 float64
Longitude                float64
Latitude                 float64
Poverty rate               int32
Terrain type              object
Roads needing repair     float64
Access to WASH           float64
Access to clean water    float64
Violent incidents        float64
Homicides                float64
dtype: object

Pandas dataframe set_index not accepting array

The problem here is passing a numpy array to set_index. Convert it to a list and it should work.

So replace

df.set_index(indexes, inplace=True)

with

df.set_index(indexes.tolist(), inplace=True)

Pandas set_index does not set the index

set_index is not inplace (unless you pass inplace=True). otherwise all correct

In [7]: df = df.set_index(pd.DatetimeIndex(df['b']))

In [8]: df
Out[8]: 
<class 'pandas.core.frame.DataFrame'>
DatetimeIndex: 100 entries, 2013-06-14 09:10:23.523845 to 2013-06-14 10:12:51.650043
Data columns (total 2 columns):
b    100  non-null values
c    100  non-null values
dtypes: datetime64[ns](1), int64(1)

also as a FYI, in forthcoming 0.12 release (next week),
you can pass unit=us to specify units of microseconds since epoch

In [13]: pd.to_datetime(a,unit='us')
Out[13]: 
<class 'pandas.tseries.index.DatetimeIndex'>
[2013-06-14 13:10:23.523845, ..., 2013-06-14 14:12:51.650043]
Length: 100, Freq: None, Timezone: None

set_index does not effectively set the index

Try to set index earlier:

data = pd.read_csv('djia_data.csv', index_col='date', parse_dates=['date'])

When i use set_index,I am not able to create a seperate dataframe with set_index column name

Since you have set 'Account' to be the index you can't select it as a column, but you only need to select the column 'd' and the dates will appear as well. To make 'Account' a column, just duplicate it from the index.

inp['account'] = inp.index
inp = inp[['account', 'd']]

problem in setting index in pandas DataFrame

To set the DataFrame index (row labels) using one or more existing columns.

You can use the DataFrame.set_index(keys, drop=True, append=False, inplace=False, verify_integrity=False)

Where the parameters define:

keys : column label or list of column labels / arrays drop : boolean,
default True

Delete columns to be used as the new index

append : boolean, default False

Whether to append columns to existing index

inplace : boolean, default False

Modify the DataFrame in place (do not create a new object)

verify_integrity : boolean, default False

Check the new index for duplicates. Otherwise defer the check until
necessary. Setting to False will improve the performance of this
method

import pandas as pd
import numpy as np

df1 = pd.DataFrame({'Country':['Nigeria','Bangladesh','China'],
                    'population':[89765,98744,654520],
                    'Birth_Rate':[23.54,34.43,20.3],
                    'Update_Date':['2016-01-18','2016-02-15','2016-02-03']},
                    columns = ['Country','population','Birth_Rate','Update_Date'])                                                        

df2 = pd.DataFrame({'Country':['India','Sri Lanka','Dubai'],
                    'population':[98343,2453,57432],
                    'Birth_Rate':[33.54,44.44,23.3],
                    'Update_Date':['2016-01-18','2016-02-15','2016-02-03']},
                    columns =['Country','population','Birth_Rate','Update_Date']) 

df3 = df2.append(df1)
df3.set_index('Country', inplace = True, 
                            append = True, drop = True)
print(df3)

OUTPUT:

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