Right Way to Reverse a Pandas Dataframe

Right way to reverse a pandas DataFrame?

data.reindex(index=data.index[::-1])

or simply:

data.iloc[::-1]

will reverse your data frame, if you want to have a for loop which goes from down to up you may do:

for idx in reversed(data.index):
    print(idx, data.loc[idx, 'Even'], data.loc[idx, 'Odd'])

or

for idx in reversed(data.index):
    print(idx, data.Even[idx], data.Odd[idx])

You are getting an error because reversed first calls data.__len__() which returns 6. Then it tries to call data[j - 1] for j in range(6, 0, -1), and the first call would be data[5]; but in pandas dataframe data[5] means column 5, and there is no column 5 so it will throw an exception. ( see docs )

reverse dataframe's rows' order with pandas

Check out http://pandas.pydata.org/pandas-docs/stable/indexing.html

You can do

reversed_df = df.iloc[::-1]

Reversing the order of only the rows in Pandas Python

You can just use a reset_index to reset your index after you've reversed it.

data=data.iloc[::-1].reset_index(drop=True)

Reverse rows in time series dataframe

NumPy has support for this via its datetime and timedelta data types.

First you reverse both columns in your time series as follows:

import pandas as pd
import numpy as np
df2 = df
df2 = df2.iloc[::-1]
df2

where df is your original time series data and df2 (shown below) is the reversed time series.

    value   date
7   66.45   2017-04-25
6   65.36   2017-03-25
5   62.13   2017-03-05
4   63.84   2017-02-12
3   64.02   2017-02-05
2   63.88   2017-01-29
1   63.95   2017-01-22
0   63.85   2017-01-15

Next you find the day differences and store them as timedelta objects:

dates_np = np.array(df2.date).astype(np.datetime64)       # Convert dates to np.datetime64 ojects
timeDeltas = np.insert(abs(np.diff(dates_np)), 0, 0)      # np.insert is to account for -1 length during np.diff call

d2 = {'value': df_reversed.value, 'day_diff': timeDeltas}     # Create new dataframe (df3)
df3 = pd.DataFrame(data=d2)
df3

where df3 (the day differences table) looks like this:

    value   day_diff
7   66.45   0 days
6   65.36   31 days
5   62.13   20 days
4   63.84   21 days
3   64.02   7 days
2   63.88   7 days
1   63.95   7 days
0   63.85   7 days

Lastly, to get back to dates accumulating from a start data, you do the following:

startDate = np.datetime64('2000-01-01')         # You can change this if you like
df4 = df2         # Copy coumn data from df2
df4.date = np.array(np.cumsum(df3.day_diff) + startDate   # np.cumsum accumulates the day_diff sum  
df4

where df4 (the start date accumulation) looks like this:

    value   date
7   66.45   2000-01-01
6   65.36   2000-02-01
5   62.13   2000-02-21
4   63.84   2000-03-13
3   64.02   2000-03-20
2   63.88   2000-03-27
1   63.95   2000-04-03
0   63.85   2000-04-10

I noticed there is a 1-day discrepancy with my final table, however this is most likely due to the implementation of timedelta inclusivity/exluclusivity.

Reverse DataFrame column order

A solution close to what you have already tried is to use:

>>> football[football.columns[::-1]]
   losses  wins     team  year
0       5    11    Bears  2010
1       8     8    Bears  2011
2       6    10    Bears  2012
3       1    15  Packers  2011
4       5    11  Packers  2012
5      10     6    Lions  2010
6       6    10    Lions  2011
7      12     4    Lions  2012

football.columns[::-1] reverses the order of the DataFrame's sequence of columns, and football[...] reindexes the DataFrame using this new sequence.

A more succinct way to achieve the same thing is with the iloc indexer:

football.iloc[:, ::-1]

The first : means "take all rows", the ::-1 means step backwards through the columns.

The loc indexer mentioned in @PietroBattiston's answer works in the same way.

Pandas DataFrame reversed rolling window

based on link1, link2, link3 you would get away with df["column_name"][::-1] or something similar

Also: you can use .rolling(num).agg(["mean", "std"]) to go through dataframe once

Working With Series in Reverse Order (Latest First)

In [28]: s = pd.Series([20, 10, 30], ['c', 'a', 'b'])

In [29]: s
Out[29]:
c    20
a    10
b    30
dtype: int64

Sorting on the index

In [30]: s.sort_index(ascending=False)
Out[30]:
c    20
b    30
a    10
dtype: int64

sorting on the values

In [31]: s.sort()

In [32]: s[::-1]
Out[32]:
b    30
c    20
a    10
dtype: int64

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