*chevron_left*Miscellaneous Cookbook

Adjusting number of rows that are printedAppending DataFrame to an existing CSV fileChecking differences between two indexesChecking if a DataFrame is emptyChecking if a variable is a DataFrameChecking if index is sortedChecking if value exists in IndexChecking memory usage of DataFrameChecking whether a Pandas object is a view or a copyConcatenating a list of DataFramesConverting a DataFrame to a listConverting a DataFrame to a SeriesConverting DataFrame to a list of dictionariesConverting DataFrame to list of tuplesCounting the number of negative valuesCreating a DataFrame using cartesian product of two DataFramesDisplaying DataFrames side by sideDisplaying full non-truncated DataFrame valuesDrawing frequency histogram of DataFrame columnExporting Pandas DataFrame to PostgreSQL tableHighlighting a particular cell of a DataFrameHighlighting DataFrame cell based on valueHow to solve "ValueError: If using all scalar values, you must pass an index"Importing BigQuery table as Pandas DataFramePlotting two columns of DataFramePrinting DataFrame on a single linePrinting DataFrame without indexPrinting DataFrames in tabular formatRandomly splitting DataFrame into multiple DataFrames of equal sizeReducing DataFrame memory sizeSaving a DataFrame as a CSV fileSaving DataFrame as Excel fileSaving DataFrame as feather fileSetting all values to zeroShowing all dtypes without truncationSplitting DataFrame into multiple DataFrames based on valueSplitting DataFrame into smaller equal-sized DataFramesWriting DataFrame to SQLite

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# Concatenating a list of DataFrames in Pandas

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**interactive map of data science**To concatenate a list of DataFrames in Pandas either vertically or horizontally, use the `concat(~)`

method.

# Vertically concatenating DataFrames

Consider the following DataFrames:

```
df1 = pd.DataFrame({"A":[2,3],"B":[4,5]})df2 = pd.DataFrame({"A":[6,7],"B":[8,9]})
A B | A B0 2 4 | 0 6 81 3 5 | 1 7 9
```

To vertically concatenate the DataFrames:

```
pd.concat([df1, df2])
A B0 2 41 3 50 6 81 7 9
```

If the column labels do not align, then some entries would be `NaN`

:

```
df1 = pd.DataFrame({"A":[2,3],"B":[4,5]})df2 = pd.DataFrame({"A":[6,7],"C":[8,9]})pd.concat([df1, df2])
A B C0 2 4.0 NaN1 3 5.0 NaN0 6 NaN 8.01 7 NaN 9.0
```

# Horizontally concatenating DataFrames

Consider the following DataFrames:

```
df1 = pd.DataFrame({"A":[2,3],"B":[4,5]})df2 = pd.DataFrame({"A":[6,7],"B":[8,9]})
A B | A B0 2 4 | 0 6 81 3 5 | 1 7 9
```

To horizontally concatenate the DataFrames:

```
pd.concat([df1, df2], axis=1)
A B A C0 2 4 6 81 3 5 7 9
```

Here, `axis=1`

is needed to perform concatenation horizontally, as opposed to vertically.

In the case when index (row labels) does not align, we end up with `NaN`

for some entries:

```
df1 = pd.DataFrame({"A":[2,3],"B":[4,5]}, index=["a","b"])df2 = pd.DataFrame({"A":[6,7],"B":[8,9]}, index=["a","c"])pd.concat([df1, df2], axis=1)
A B A Ba 2.0 4.0 6.0 8.0b 3.0 5.0 NaN NaNc NaN NaN 7.0 9.0
```

Published by Isshin Inada

Edited by 0 others

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